Method, apparatus, server and system for vital sign detection and monitoring

ABSTRACT

Methods, apparatus and systems for detecting and monitoring vital signs and other periodic motions of an object are disclosed. In one example, a system for monitoring object motion in a venue is disclosed. The system comprises a transmitter, a receiver, and a vital sign estimator. The transmitter is located at a first position in the venue and configured for transmitting a wireless signal through a wireless multipath channel impacted by a pseudo-periodic motion of an object in the venue. The receiver is located at a second position in the venue and configured for: receiving the wireless signal through the wireless multipath channel impacted by the pseudo-periodic motion of the object in the venue, and obtaining at least one time series of channel information (TSCI) of the wireless multipath channel based on the wireless signal. The vital sign estimator is configured for: determining that at least one portion of the at least one TSCI in a current sliding time window is associated with the pseudo-periodic motion of the object in the venue, and computing a current characteristics related to the pseudo-periodic motion of the object in the current sliding time window based on at least one of: the at least one portion of the at least one TSCI in the current sliding time window, at least one portion of the at least one TSCI in a past sliding time window, and a past characteristics related to the pseudo-periodic motion of the object in the past sliding time window.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application hereby claims priority to, and incorporates byreference the entirety of the disclosures of, each of the followingapplications:

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No. 14/605,611, entitled “WIRELESS            POSITIONING SYSTEMS”, filed on Jan. 26, 2015, published as            US2016/0018508A1 on Jan. 21, 2016,            -   1. which claims priority to U.S. Provisional patent                application 62/025,795, entitled “TIME-REVERSAL                POSITIONING SYSTEMS”, filed on Jul. 17, 2014, and            -   2. which claims priority to U.S. Provisional patent                application 62/069,090, entitled “TIME-REVERSAL                POSITIONING SYSTEMS”, filed on Oct. 27, 2014,-   (b) U.S. patent application Ser. No. 15/584,052, entitled “METHOD,    SYSTEM, AND APPARATUS FOR WIRELESS POWER TRANSMISSION BASED ON POWER    WAVEFORMING”, filed on May 2, 2017,    -   (1) which claims priority to U.S. Provisional patent application        62/331,278, entitled “USING VIRTUAL ANTENNAS FOR POWER        WAVEFORMING IN WIRELESS POWER TRANSMISSION SYSTEMS”, filed on        May 3, 2016,-   (c) U.S. patent application Ser. No. 15/434,813, entitled “METHODS,    DEVICES, APPARATUS, AND SYSTEMS FOR MEDIUM ACCESS CONTROL IN    WIRELESS COMMUNICATION SYSTEMS UTILIZING SPATIAL FOCUSING EFFECT”,    filed on Feb. 16, 2017,    -   (1) which claims priority to U.S. Provisional patent application        62/295,970, entitled “THE IMPACT OF SPATIAL FOCUSING EFFECTS ON        MEDIUM ACCESS CONTROL DESIGN FOR 5G”, filed on Feb. 16, 2016,    -   (2) which claims priority to U.S. Provisional patent application        62/320,965, entitled “OPTIMAL RATE ADAPTATION FOR THROUGHPUT        MAXIMIZATION IN TIME REVERSAL DIVISION MULTIPLE ACCESS”, filed        on Apr. 11, 2016,-   (d) PCT patent application PCT/US2017/021963, entitled “METHODS,    APPARATUS, SERVERS, AND SYSTEMS FOR VITAL SIGNS DETECTION AND    MONITORING”, filed on Mar. 10, 2017, published as WO2017/156492A1 on    Sep. 14, 2017,    -   (1) which claims priority to U.S. Provisional patent application        62/307,081, entitled “TR-BREATH: TIME-REVERSAL BREATHING RATE        ESTIMATION AND DETECTION”, filed on Mar. 11, 2016,    -   (2) which claims priority to U.S. Provisional patent application        62/316,850, entitled “TR-BREATH: TIME-REVERSAL BREATHING RATE        ESTIMATION AND DETECTION”, filed on Apr. 1, 2016,-   (e) PCT patent application PCT/US2017/021957, entitled “METHODS,    APPARATUS, SERVERS, AND SYSTEMS FOR HUMAN IDENTIFICATION BASED ON    HUMAN RADIO BIOMETRIC INFORMATION”, filed on Mar. 10, 2017,    published as WO2017/156487A1 on Sep. 14, 2017,    -   (1) which claims priority to U.S. Provisional patent application        62/307,172, entitled “RADIO SHOT: THROUGH-THE-WALL HUMAN        IDENTIFICATION”, filed on Mar. 11, 2016,    -   (2) which claims priority to U.S. Provisional patent application        62/334,110, entitled “TIME-REVERSAL TRACKING WITHOUT MAPPING”,        filed on May 10, 2016,-   (f) PCT patent application PCT/US2017/027131, entitled “METHODS,    APPARATUS, SERVERS, AND SYSTEMS FOR OBJECT TRACKING”, filed on Apr.    12, 2017, published as WO2017/180698A1 on Oct. 19, 2017,    -   (1) which claims priority to U.S. Provisional patent application        62/322,575, entitled “TIME-REVERSAL RESONATING EFFECT AND ITS        APPLICATION IN WALKING SPEED ESTIMATION”, filed on Apr. 14,        2016,    -   (2) which claims priority to U.S. Provisional patent application        62/334,110, entitled “TIME-REVERSAL TRACKING WITHOUT MAPPING”,        filed on May 10, 2016, and    -   (3) which claims priority to U.S. Provisional patent application        62/409,796, entitled “METHODS, DEVICES, SERVERS, AND SYSTEMS OF        TIME REVERSAL BASED TRACKING”, filed on Oct. 18, 2016,-   (g) U.S. Provisional patent application 62/557,117, entitled    “METHODS, DEVICES, SERVERS, APPARATUS, AND SYSTEMS FOR WIRELESS    INTERNET OF THINGS APPLICATIONS”, filed on Sep. 11, 2017,-   (h) U.S. Provisional patent application 62/593,826, entitled    “METHOD, APPARATUS, AND SYSTEM FOR OBJECT TRACKING AND NAVIGATION”,    filed on Dec. 1, 2017,-   (i) U.S. patent application Ser. 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No. 15/061,059, entitled “TIME-REVERSAL SCALABILITY FOR        HIGH NETWORK DENSIFICATION”, filed on Mar. 4, 2016,        -   a. which claims priority to U.S. Provisional patent            application 62/128,574, entitled “TIME-REVERSAL SCALABILITY            FOR HIGH NETWORK DENSIFICATION”, filed on Mar. 5, 2015,    -   (11) which is a Continuation-in-Part of PCT patent application        PCT/US2015/041037, entitled “WIRELESS POSITIONING SYSTEMS”,        filed on Jul. 17, 2015, published as WO2016/011433A2 on Jan. 21,        2016,        -   a. which claims priority to U.S. Provisional patent            application 62/148,019, entitled “WIRELESS POSITIONING            SYSTEMS”, filed on Apr. 15, 2015,        -   b. which is a continuation-in-part of U.S. patent            application Ser. No. 14/605,611, entitled “WIRELESS            POSITIONING SYSTEMS”, filed on Jan. 26, 2015, published as            US2016/0018508A1 on Jan. 21, 2016,            -   1. which claims priority to U.S. Provisional patent                application 62/025,795 entitled “TIME-REVERSAL                POSITIONING SYSTEMS”, filed on Jul. 17, 2014, and            -   2. which claims priority to U.S. Provisional patent                application 62/069,090 entitled “TIME-REVERSAL                POSITIONING SYSTEMS”, filed on Oct. 27, 2014,    -   (12) which is a Continuation-in-Part of U.S. patent application        Ser. No. 15/268,477, entitled “METHODS, DEVICES AND SYSTEMS OF        HETEROGENEOUS TIME-REVERSAL PARADIGM ENABLING DIRECT        CONNECTIVITY IN INTERNET OF THINGS”, filed on Sep. 16, 2016,        issued as U.S. Pat. No. 9,887,864 on Feb. 6, 2018,        -   a. which claims priority to U.S. Provisional patent            application 62/219,315, entitled “ENABLING DIRECT            CONNECTIVITY IN INTERNET OF THINGS: A HETEROGENEOUS            TIME-REVERSAL PARADIGM”, filed on Sep. 16, 2015,        -   b. which is a Continuation-in-part of U.S. patent            application Ser. No. 15/200,429, entitled “TIME-REVERSAL            WIRELESS PARADIGM FOR INTERNET OF THINGS”, filed on Jul. 1,            2016, issued as U.S. Pat. No. 9,781,700 on Oct. 3, 2017,            -   1. which is a Continuation of U.S. patent application                Ser. No. 14/943,648, entitled “TIME-REVERSAL WIRELESS                PARADIGM FOR INTERNET OF THINGS”, filed on Nov. 17,                2015, issued as U.S. Pat. No. 9,402,245 on Jul. 26,                2016,                -   i. which is a Continuation of U.S. patent                    application Ser. No. 14/202,651, entitled                    “TIME-REVERSAL WIRELESS PARADIGM FOR INTERNET OF                    THINGS”, filed on Mar. 10, 2014, issued as U.S. Pat.                    No. 9,226,304 on Dec. 29, 2015,    -   (13) which is a Continuation-in-Part of U.S. patent application        Ser. No. 15/284,496, entitled “TIME-REVERSAL COMMUNICATION        SYSTEMS”, filed on Oct. 3, 2016,        -   a. which claims priority to U.S. Provisional patent            application 62/235,958, entitled “SYMBOL TIMING FOR            TIME-REVERSAL SYSTEMS WITH SIGNATURE DESIGN”, filed on Oct.            1, 2015,    -   (14) which is a Continuation-in-Part of        -   PCT patent application PCT/US2016/066015, entitled “METHOD,            APPARATUS, AND SYSTEMS FOR WIRELESS EVENT DETECTION AND            MONITORING”, filed on Dec. 9, 2016, published as            WO2017/100706A1 on Jun. 15, 2017, whose US national stage            entry is 16/060,710, filed on Jun. 8, 2018,        -   a. which claims priority to U.S. Provisional patent            application 62/265,155, entitled “INDOOR EVENTS DETECTION            SYSTEM”, filed on Dec. 9, 2015,        -   b. which claims priority to U.S. Provisional patent            application 62/411,504, entitled “METHOD, APPARATUS, AND            SYSTEM FOR WIRELESS INTERNET OF THINGS APPLICATIONS”, filed            on Oct. 21, 2016,        -   c. which claims priority to U.S. Provisional patent            application 62/383,235, entitled “TIME REVERSAL MONITORING            SYSTEM”, filed on Sep. 2, 2016,        -   d. which claims priority to U.S. Provisional patent            application 62/307,081, entitled “TR-BREATH: TIME-REVERSAL            BREATHING RATE ESTIMATION AND DETECTION”, filed on Mar. 11,            2016,        -   e. which claims priority to U.S. Provisional patent            application 62/316,850, entitled “TR-BREATH: TIME-REVERSAL            BREATHING RATE ESTIMATION AND DETECTION”, filed on Apr. 1,            2016,    -   (15) which claims priority to U.S. Provisional patent        application 62/331,278, entitled “USING VIRTUAL ANTENNAS FOR        POWER WAVEFORMING IN WIRELESS POWER TRANSMISSION SYSTEMS”, filed        on May 3, 2016,    -   (16) which claims priority to U.S. Provisional patent        application 62/295,970, entitled “THE IMPACT OF SPATIAL FOCUSING        EFFECTS ON THE MEDIUM ACCESS CONTROL DESIGN FOR 5G”, filed on        Feb. 16, 2016,    -   (17) which claims priority to U.S. Provisional patent        application 62/320,965, entitled “OPTIMAL RATE ADAPTATION FOR        THROUGHPUT MAXIMIZATION IN TIME REVERSAL DIVISION MULTIPLE        ACCESS”, filed on Apr. 11, 2016,    -   (18) which claims priority to U.S. Provisional patent        application 62/307,081, entitled “TR-BREATH: TIME-REVERSAL        BREATHING RATE ESTIMATION AND DETECTION”, filed on Mar. 11,        2016,    -   (19) which claims priority to U.S. Provisional patent        application 62/316,850, entitled “TR-BREATH: TIME-REVERSAL        BREATHING RATE ESTIMATION AND DETECTION”, filed on Apr. 1, 2016,    -   (20) which claims priority to U.S. Provisional patent        application 62/307,172, entitled “RADIO SHOT: THROUGH-THE-WALL        HUMAN IDENTIFICATION”, filed on Mar. 11, 2016,    -   (21) which claims priority to U.S. Provisional patent        application 62/322,575, entitled “TIME-REVERSAL RESONATING        EFFECT AND ITS APPLICATION IN WALKING SPEED ESTIMATION”, filed        on Apr. 14, 2016,    -   (22) which claims priority to U.S. Provisional patent        application 62/334,110, entitled “TIME-REVERSAL TRACKING WITHOUT        MAPPING”, filed on May 10, 2016,    -   (23) which claims priority to U.S. Provisional patent        application 62/409,796, entitled “METHODS, DEVICES, SERVERS, AND        SYSTEMS OF TIME REVERSAL BASED TRACKING”, filed on Oct. 18,        2016,    -   (24) which claims priority to U.S. Provisional patent        application 62/383,235, entitled “TIME REVERSAL MONITORING        SYSTEM”, filed on Sep. 2, 2016,    -   (25) which claims priority to U.S. Provisional patent        application 62/384,060, entitled “METHODS, DEVICES, SERVERS,        SYSTEMS OF TIME REVERSAL MACHINE PLATFORM FOR BROADBAND WIRELESS        APPLICATIONS”, filed on Sep. 6, 2016,    -   (26) which claims priority to U.S. Provisional patent        application 62/411,504, entitled “METHOD, APPARATUS, AND SYSTEM        FOR WIRELESS INTERNET OF THINGS APPLICATIONS”, filed on Oct. 21,        2016,-   (j) PCT patent application PCT/US2017/015909, entitled “METHODS,    DEVICES, SERVERS, APPARATUS, AND SYSTEMS FOR WIRELESS INTERNET OF    THINGS APPLICATIONS”, filed on Jan. 31, 2017, published as    WO2017/155634A1 on Sep. 14, 2017,    -   (1) which claims priority to U.S. Provisional patent application        62/384,060, entitled “METHODS, DEVICES, SERVERS, SYSTEMS OF TIME        REVERSAL MACHINE PLATFORM FOR BROADBAND WIRELESS APPLICATIONS”,        filed on Sep. 6, 2016,    -   (2) which claims priority to U.S. Provisional patent application        62/331,278, entitled “USING VERTUAL ANTENNAS FOR POWER        WAVEFORMING IN WIRELESS POWER TRANSMISSION SYSTEMS”, filed on        May 3, 2016,    -   (3) which claims priority to U.S. Provisional patent application        62/307,081, entitled “TR-BREATH: TIME-REVERSAL BREATHING RATE        ESTIMATION AND DETECTION”, filed on Mar. 11, 2016,    -   (4) which claims priority to U.S. Provisional patent application        62/316,850, entitled “TR-BREATH: TIME-REVERSAL BREATHING RATE        ESTIMATION AND DETECTION”, filed on Apr. 1, 2016,    -   (5) which claims priority to U.S. Provisional patent application        62/322,575, entitled “TIME-REVERSAL RESONATING EFFECT AND ITS        APPLICATION IN WALKING SPEED ESTIMATION”, filed on Apr. 14,        2016,    -   (6) which claims priority to U.S. Provisional patent application        62/334,110, entitled “TIME-REVERSAL TRACKING WITHOUT MAPPING”,        filed on May 10, 2016,    -   (7) which claims priority to U.S. Provisional patent application        62/409,796, entitled “METHODS, DEVICES, SERVERS, AND SYSTEMS OF        TIME REVERSAL BASED TRACKING”, filed on Oct. 18, 2016,    -   (8) which claims priority to U.S. Provisional patent application        62/383,235, entitled “TIME REVERSAL MONITORING SYSTEM”, filed on        Sep. 2, 2016,    -   (9) which claims priority to U.S. Provisional patent application        62/411,504, entitled “METHOD, APPARATUS, AND SYSTEM FOR WIRELESS        INTERNET OF THINGS APPLICATIONS”, filed on Oct. 21, 2016,    -   (10) which claims priority to U.S. Provisional patent        application 62/307,172, entitled “RADIO SHOT: THROUGH-THE-WALL        HUMAN IDENTIFICATION”, filed on Mar. 11, 2016,    -   (11) which claims priority to PCT patent application        PCT/US2016/066015, entitled “METHOD, APPARATUS, AND SYSTEMS FOR        WIRELESS EVENT DETECTION AND MONITORING”, filed on Dec. 9, 2016,        published as WO2017/100706A1 on Jun. 15, 2017, whose US national        stage entry is U.S. patent application Ser. No. 16/060,710,        entitled “METHOD, APPARATUS, AND SYSTEMS FOR WIRELESS EVENT        DETECTION AND MONITORING”, filed on Jun. 8, 2018,-   (k) U.S. Provisional patent application 62/678,207, entitled    “METHOD, APPARATUS, AND SYSTEM FOR OBJECT TRACKING AND MOTION    MONITORING”, filed on May 30, 2018,-   (l) U.S. patent application Ser. No. 15/861,422, entitled “METHOD,    APPARATUS, SERVER, AND SYSTEMS OF TIME-REVERSAL TECHNOLOGY”, filed    on Jan. 3, 2018,-   (m) U.S. patent application Ser. No. 15/873,806, entitled “METHOD,    APPARATUS, AND SYSTEM FOR OBJECT TRACKING AND NAVIGATION”, filed on    Jan. 17, 2018.-   (n) U.S. patent application Ser. No. 16/101,444, entitled “METHOD,    APPARATUS, AND SYSTEM FOR WIRELESS MOTION MONITORING”, filed on Aug.    11, 2018.-   (o) U.S. Provisional Patent application 62/734,224, entitled    “METHOD, APPARATUS, AND SYSTEM FOR WIRELESS SLEEP MONITORING”, filed    on Sep. 20, 2018.-   (p) U.S. Provisional Patent application 62/744,093, entitled    “METHOD, APPARATUS, AND SYSTEM FOR WIRELESS PROXIMITY AND PRESENCE    MONITORING”, filed on Oct. 10, 2018.-   (q) U.S. Provisional Patent application 62/753,017, entitled    “METHOD, APPARATUS, AND SYSTEM FOR HUMAN IDENTIFICATION BASED ON    HUMAN RADIO BIOMETRIC INFORMATION”, filed on Oct. 30, 2018.

TECHNICAL FIELD

The present teaching generally relates to vital sign detection andmonitoring. More specifically, the present teaching relates to detectingand monitoring vital signs and other periodic motions of an object basedon wireless channel information in a rich-scattering environment.

BACKGROUND

Vital signs are important indicators of a person's health and well-beingas well as predictors of acute medical conditions and chronic diseasestates for a person. Breathing rate is one of the most important vitalsigns, which can be measured by the number of exhalation and inhalationa person takes per minute. In addition, the breathing pattern may behighly correlated to psychological conditions of a human being, such asstress and anxiety.

Many important human vital signs such as breathing are periodic motions.Most traditional approaches for breathing monitoring are invasive inthat they need physical contact of the human bodies. For instance, inhospitals, the patients are required to wear oxygen masks, Nasalcannulas, chest straps, or wearable sensors such as thermistors andpressure sensors. Another example is Polysomnography (PSG) used in sleepmedicine, which typically requires a minimum of 22 wire attachments tothe patient. These dedicated devices are often costly and bulky, creatediscomfort to the human bodies, and are limited only to clinicalsettings. Although these wired medical systems can measure breathingusing wearables attached onto human body, such systems are clumsy anduncomfortable. To make it worse, the systems themselves would itselfdistort the very breathing that need to be monitored—as many patientsbecome anxious or annoyed with all the attached wearables and wires.

Currently existing non-invasive (contact-free) breathing monitoringsolutions can be categorized as below.

(1) Radar-based breathing monitoring: Doppler radars are often used inbreathing monitoring. They are operated by transmitting a signal andreceiving a signal with a Doppler shift due to a periodic motion ofobjects. The breathing rates can be extracted from the Doppler shift. Asa drawback, these systems use high transmission power, rely onsophisticated and expensive hardware, and use extremely largebandwidths. A vital sign monitoring system was disclosed utilizingfrequency modulated continuous radar (FMCW). It used Universal SoftwareRadio Peripheral (USRP) as the RF front-end to transmit afrequency-sweeping signal. But the additional cost and complexity of thededicated hardware limited a large-scale deployment of FMCW radar.

(2) Wireless-sensor based breathing monitoring: The received signalstrength (RSS) measurements from 802.15.4 compliant sensors on multiple802.15.4 channels were also used for breathing detection and breathingrate estimation. Dense deployment of wireless sensors is required inthese methods as additional wireless infrastructures. In addition, thespecific design of frequency-hopping mechanism is required to supportmultiple channel measurements.

(3) Wi-Fi-based breathing monitoring: RSS is commonly used in theWi-Fi-based breathing monitoring due to its availability on mostcommercial Wi-Fi network interface controllers (NICs). Measurements werealso used with Wi-Fi devices for breathing estimation. But this methodis accurate only when the users hold the Wi-Fi-enabled devices in closeproximity to their chests.

In addition to the drawbacks mentioned above, methods (1) and (2)require design and manufacturing of special devices such as specializedradar devices or sensor network nodes, while method (3) has very lowaccuracy and sensitivity. A wireless breathing monitoring system wasproposed to monitor breathing rate and heart rate based on UWB(ultra-wide band) signal. But the UWB-based system has many limitationssuch as: it has expensive, untested, uncommon, dedicated, limitededition experimental hardware components; it has a small range due tosevere absorption of UWB signals by walls; it works only in aline-of-sight (LOS) condition; and it needs very tedious and laboriousdeployment of many sensors in practical situations to cover a reasonablearea, making installation, maintenance and repair very expensive andlabor intensive due to the restrictive LOS operation.

Therefore, there is a need for methods and apparatus for vital signdetection and monitoring to solve the above-mentioned problems and toavoid the above-mentioned drawbacks.

SUMMARY

The present teaching generally relates to periodic motion (e.g. humanbreathing) detection. More specifically, the present teaching relates toperiodic motion detection and monitoring based on time-reversaltechnology in a rich-scattering wireless environment, such as an indoorenvironment or urban metropolitan area, enclosed environment,underground environment, open-air venue with barriers such as parkinglot, storage, yard, square, forest, cavern, valley, etc.

In one embodiment, a system for monitoring object motion in a venue isdisclosed. The system comprises a transmitter, a receiver, and a vitalsign estimator. The transmitter is located at a first position in thevenue and configured for transmitting a wireless signal through awireless multipath channel impacted by a pseudo-periodic motion of anobject in the venue. The receiver is located at a second position in thevenue and configured for: receiving the wireless signal through thewireless multipath channel impacted by the pseudo-periodic motion of theobject in the venue, and obtaining at least one time series of channelinformation (CI) of the wireless multipath channel based on the wirelesssignal. The vital sign estimator is configured for: determining that atleast one portion of the at least one time series of CI (TSCI) in acurrent sliding time window is associated with the pseudo-periodicmotion of the object in the venue, and computing a currentcharacteristics related to the pseudo-periodic motion of the object inthe current sliding time window based on at least one of: the at leastone portion of the at least one TSCI in the current sliding time window,at least one portion of the at least one TSCI in a past sliding timewindow, and a past characteristics related to the pseudo-periodic motionof the object in the past sliding time window.

In another embodiment, a method for monitoring object motion in a venueis disclosed. The method comprises: receiving a wireless signal througha wireless multipath channel impacted by a pseudo-periodic motion of anobject in the venue; obtaining at least one time series of channelinformation (CI) of the wireless multipath channel based on the wirelesssignal; determining that at least one portion of the at least one timeseries of CI (TSCI) in a current sliding time window is associated withthe pseudo-periodic motion of the object in the venue; and computing acurrent characteristics related to the pseudo-periodic motion of theobject in the current sliding time window based on at least one of: theat least one portion of the at least one TSCI in the current slidingtime window, at least one portion of the at least one TSCI in a pastsliding time window, and a past characteristics related to thepseudo-periodic motion of the object in the past sliding time window.

In yet another embodiment, a receiver of a motion monitoring system isdisclosed. The motion monitoring system comprises: a transmitter, thereceiver, and a vital sign estimator. The receiver comprises: a wirelesscircuitry, a processor communicatively coupled with the wirelesscircuitry, a memory communicatively coupled with the processor, and aset of instructions stored in the memory. The wireless circuitry isconfigured to receive a wireless signal through a wireless multipathchannel impacted by a pseudo-periodic motion of an object in a venue,wherein the wireless signal is transmitted asynchronously by thetransmitter. The set of instructions, when executed, causes theprocessor to obtain at least one time series of channel information (CI)of the wireless multipath channel based on the wireless signal. At leastone portion of the at least one time series of CI (TSCI) in a currentsliding time window is associated with the pseudo-periodic motion of theobject in the venue. The at least one portion of the at least one TSCIin the current sliding time window is to be used by the vital signestimator to compute a current characteristics related to thepseudo-periodic motion of the object in the current sliding time windowbased on at least one of: the at least one portion of the at least oneTSCI in the current sliding time window, at least one portion of the atleast one TSCI in a past sliding time window, and a past characteristicsrelated to the pseudo-periodic motion of the object in the past slidingtime window.

In still another embodiment, an estimator of a motion monitoring systemis disclosed. The motion monitoring system comprises: a transmitter, areceiver, and the estimator. The estimator comprises: a processor, amemory communicatively coupled with the processor, and a set ofinstructions stored in the memory. The set of instructions, whenexecuted, causes the processor to perform: obtaining at least one timeseries of channel information (TSCI) of a wireless multipath channelfrom a receiver of the motion monitoring system, wherein the receiverextracts the at least one TSCI from a wireless signal received from atransmitter of the motion monitoring system through the wirelessmultipath channel impacted by a pseudo-periodic motion of an object in avenue, determining that at least one portion of the at least one TSCI ina current sliding time window is associated with the pseudo-periodicmotion of the object in the venue, and computing a currentcharacteristics related to the pseudo-periodic motion of the object inthe current sliding time window based on at least one of: the at leastone portion of the at least one TSCI in the current sliding time window,at least one portion of the at least one TSCI in a past sliding timewindow, and a past characteristics related to the pseudo-periodic motionof the object in the past sliding time window.

In a different embodiment, a system for monitoring a repeating motion ina venue is disclosed. The system comprises a transmitter, a receiver,and a repeating motion monitor. The transmitter is located at a firstposition in the venue and configured for transmitting a wireless signalthrough a wireless multipath channel impacted by the repeating motion ofan object in the venue. The receiver is located at a second position inthe venue and configured for: receiving the wireless signal through thewireless multipath channel impacted by the repeating motion of theobject in the venue, and obtaining a time series of channel information(CI) of the wireless multipath channel based on the wireless signal. Therepeating motion monitor is configured for: monitoring a periodiccharacteristics of the repeating motion of the object based on the timeseries of CI.

In another embodiment, a method for monitoring a repeating motion in avenue is disclosed. The method comprises: receiving a wireless signalthrough a wireless multipath channel impacted by the repeating motion ofan object in the venue; obtaining a time series of channel information(CI) of the wireless multipath channel based on the wireless signal; andmonitoring a periodic characteristics of the repeating motion of theobject based on the time series of CI.

In yet another embodiment, a receiver of a motion monitoring system isdisclosed. The motion monitoring system comprises: a transmitter, thereceiver, and a repeating motion monitor. The receiver comprises: awireless circuitry, a processor communicatively coupled with thewireless circuitry, a memory communicatively coupled with the processor,and a set of instructions stored in the memory. The wireless circuitryis configured to receive a wireless signal through a wireless multipathchannel impacted by a repeating motion of an object in a venue. Thewireless signal is transmitted by the transmitter. The set ofinstructions, when executed, causes the processor to obtain a timeseries of channel information (CI) of the wireless multipath channelbased on the wireless signal. The time series of CI is to be used by arepeating motion monitor of the motion monitoring system to monitor aperiodic characteristics of the repeating motion.

In still another embodiment, a monitor of a motion monitoring system isdisclosed. The motion monitoring system comprises: a transmitter, areceiver, and the monitor. The monitor comprises: a processor, a memorycommunicatively coupled with the processor, and a set of instructionsstored in the memory. The set of instructions, when executed, causes theprocessor to perform: obtaining a time series of channel information(CI) of a wireless multipath channel from a receiver of the motionmonitoring system, wherein the receiver extracts the time series of CIfrom a wireless signal received from a transmitter of the motionmonitoring system through the wireless multipath channel impacted by arepeating motion of an object in a venue, and monitoring a periodiccharacteristics of the repeating motion of the object based on the timeseries of CI.

Other concepts are related to software for implementing the presentteaching on detecting and monitoring vital signs and other periodicmotions based on wireless channel information in a rich-scatteringenvironment.

Additional novel features will be set forth in part in the descriptionwhich follows, and in part will become apparent to those skilled in theart upon examination of the following and the accompanying drawings ormay be learned by production or operation of the examples. The novelfeatures of the present teachings may be realized and attained bypractice or use of various aspects of the methodologies,instrumentalities and combinations set forth in the detailed examplesdiscussed below.

BRIEF DESCRIPTION OF DRAWINGS

The methods, systems, and/or programming described herein are furtherdescribed in terms of exemplary embodiments. These exemplary embodimentsare described in detail with reference to the drawings. Theseembodiments are non-limiting exemplary embodiments, in which likereference numerals represent similar structures throughout the severalviews of the drawings.

FIG. 1 illustrates an exemplary network environment for vital signdetection and monitoring in a venue, according to one embodiment of thepresent teaching.

FIG. 2 illustrates an exemplary block diagram of a system for vital signdetection and monitoring, according to one embodiment of the presentteaching.

FIG. 3 illustrates an exemplary block diagram of a vital sign estimatorfor vital sign detection and monitoring, according to one embodiment ofthe present teaching.

FIG. 4 illustrates a flowchart of an exemplary method for vital signdetection and monitoring, according to one embodiment of the presentteaching.

FIG. 5 illustrates an exemplary finite state machine (FSM) forsingle-person breathing monitoring, according to one embodiment of thepresent teaching.

FIG. 6 illustrates an exemplary procedure for FSMs associated withdifferent people selecting peaks, according to one embodiment of thepresent teaching.

FIG. 7 illustrates a flowchart of an exemplary method for vital signestimation, according to one embodiment of the present teaching.

FIG. 8 illustrates an exemplary system state controller which is a FSMfor single-person breathing monitoring, according to one embodiment ofthe present teaching.

FIG. 9 illustrates an exemplary state transition of FSM forsingle-person breathing monitoring, according to one embodiment of thepresent teaching.

FIG. 10 illustrates an exemplary experiment result on comparison betweenschemes with and without FSM with a running fan, according to oneembodiment of the present teaching.

FIG. 11 illustrates an exemplary state transition of the FSM scheme withan operating fan, according to one embodiment of the present teaching.

FIG. 12 illustrates an exemplary breathing estimation with subjectstanding up for five seconds, according to one embodiment of the presentteaching.

FIG. 13 illustrates an exemplary method for vital sign detection andmonitoring, according to one embodiment of the present teaching.

FIG. 14 illustrates an exemplary block diagram of a repeating motionmonitor, according to one embodiment of the present teaching.

FIG. 15 illustrates a flowchart of an exemplary method for estimatingand monitoring repetition rate (e.g. breathing rate), according to oneembodiment of the present teaching.

FIG. 16 illustrates exemplary autocorrelation functions of the receivedsignals under different scenarios, according to one embodiment of thepresent teaching.

FIG. 17 illustrates exemplary features extracted from the derivedautocorrelation functions for breathing detection and estimation,according to one embodiment of the present teaching.

FIG. 18 illustrates an exemplary scheme for breathing signal extractionand maximization, according to one embodiment of the present teaching.

FIG. 19 illustrates an exemplary procedure of multi-person repetitionrate (e.g. breathing rate) estimation, according to one embodiment ofthe present teaching.

FIG. 20 illustrates an exemplary diagram of a device in a motionmonitoring system, according to one embodiment of the present teaching.

FIG. 21 illustrates an exemplary breathing signal based on real-worldmeasurements, according to one embodiment of the present teaching.

FIG. 22 demonstrates gains of a disclosed scheme for breathing signalextraction and maximization, according to one embodiment of the presentteaching.

FIG. 23A and FIG. 23B illustrate comparison results between a wake stateand a sleep state, according to one embodiment of the present teaching.

FIG. 24A and FIG. 24B illustrate breathing rate performances ofdifferent sleep stages, according to one embodiment of the presentteaching.

FIG. 25 illustrates an exemplary network environment for sleepmonitoring, according to one embodiment of the present teaching.

FIG. 26 illustrates an exemplary algorithm design for sleep monitoring,according to one embodiment of the present teaching.

FIG. 27 illustrates an exemplary interior bird-view of a car for seatoccupancy detection and people counting, according to one embodiment ofthe present teaching.

FIGS. 28A-28D illustrate changes of channel information according tovarious seat occupancy situations in a car, according to one embodimentof the present teaching.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant teachings. However, it should be apparent to those skilledin the art that the present teachings may be practiced without suchdetails. In other instances, well known methods, procedures, components,and/or circuitry have been described at a relatively high-level, withoutdetail, in order to avoid unnecessarily obscuring aspects of the presentteachings.

The present teaching discloses systems, apparatus, and methods fordetecting and monitoring a periodic or pseudo-periodic object motion ina venue based on a time series of channel information of a wirelessmultipath channel that is impacted by the object motion. According tovarious embodiments, the object may be a life (e.g. a human, a pet, ananimal, etc.), a device (e.g. a fan, a machine, etc.), or a land; theperiodic or pseudo-periodic object motion may represent: breathing,heartbeat, a periodic hand gesture, a periodic gait, a rotation, avibration, or an earthquake; and the disclosed system can monitorcharacteristics of the motion, e.g. a rate or frequency of the motion.In the present teaching, the term “pseudo-periodic motion” refers to aperiodic motion with slight perturbation, such as a periodic motion withincreasing or decreasing frequency, or a combination/sum of two periodicmotions with different frequencies. For simplicity, the followingdescription will focus on methods for detecting and monitoringpseudo-periodic motions, while the same disclosed methods can be appliedto periodic motion detection and monitoring as well.

According to various embodiments, each channel information (CI) maycomprise at least one of: a channel state information (CSI), a frequencydomain CSI, a frequency domain CSI associated with at least onesub-band, a time domain CSI, a CSI in a domain, a channel impulseresponse (CIR), a channel frequency response (CFR), a channelcharacteristics, a channel filter response, a CSI of the wirelessmultipath channel, an information of the wireless multipath channel, atime stamp, an auxiliary information, a data, a meta data, a user data,an account data, an access data, a security data, a session data, astatus data, a supervisory data, a household data, an identity (ID), adevice data, a network data, a neighborhood data, an environment data, areal-time data, a sensor data, a stored data, an encrypted data, acompressed data, a protected data, and/or another channel information.In one embodiment, the disclosed system has hardware components (e.g.wireless transmitter/receiver with antenna, analog circuitry, powersupply, processor, memory, etc.) and corresponding software components.According to various embodiments of the present teaching, the disclosedsystem includes a Bot (referred to as a Type 1 device) and an Origin(referred to as a Type 2 device) for vital sign detection andmonitoring. Each device comprises a transceiver, a processor and amemory.

In one embodiment, a robust vital sign monitoring system is disclosed toanalyze the tiny temporal variations in the CFRs. On each antenna link,the system transforms CFRs into CIRs and performs spectral analysis onthe CIRs via fast Fourier transform (FFT). The spectrum of differentantenna links are then fused into one averaged spectrum. Then,persistence-based peak detection is performed on the spectrum whichfinally leads to the repetition rate (e.g. breathing rate) estimation.Furthermore, FSM is introduced into the disclosed system such that thevital sign monitoring system would adopt different thresholds indifferent states, which significantly improves the robustness of vitalsign monitoring. Experimental results validate that the system couldperform well for single-person as well as multi-person breathingtracking.

In one embodiment, the disclosed system uses CSI from regularoff-the-shelf WiFi chips which are affordable, widely available,standard compliant, interoperable, FCC approved, with quality controland technical support by reputable brand. These components (WiFi chips,modules, design tools, knowhow) are much more affordable, much morewidely available and much richer than the expensive, untested, uncommon,dedicated, limited edition experimental hardware components of anexisting system. The disclosed system has a large effective range, dueto the large WiFi coverage. The disclosed system can work in bothline-of-sight (LOS) and non-line-of-sight (NLOS) conditions. Thedisclosed system may also use CSI from chips associated with 4G, LTE,LTE-U, 5G and beyond.

The disclosed system includes features that are significantly more thanan abstract idea. To measure human breathing remotely without wearableshas been a dream for decades. The disclosed system solves the long-timeproblem using physical WiFi chips as sensors to monitor the environmentwith WiFi signals and the associated multipath patterns. The disclosedsystem has hardware components (e.g. Type 1 device, Type 2 device eachwith processor, memory, wireless transceivers, cloud server, etc.). TheWiFi signals are electromagnetic (EM) waves in the air which aremeasurable and with deterministic structure and frequencycharacteristics. The system has matching software components (e.g.embedded software in Type 1 device, in Type 2 device, in servers, etc.).The software can interface with low-level WiFi chips and firmware toextract CSI. Experimental results show that the disclosed system candetect human breathing rate with very high accuracy.

The disclosed system can be applied in many cases. In one example, theType 1 device (transmitter) may be a small WiFi-enabled device restingon the table. It may also be a WiFi-enabled television (TV), set-top box(STB), a smart speaker (e.g. Amazon echo), a smart refrigerator, a smartmicrowave oven, a mesh network router, a mesh network satellite, a smartphone, a computer, a tablet, a smart plug, etc. In one example, the Type2 (receiver) may be a WiFi-enabled device resting on the table. It mayalso be a WiFi-enabled television (TV), set-top box (STB), a smartspeaker (e.g. Amazon echo), a smart refrigerator, a smart microwaveoven, a mesh network router, a mesh network satellite, a smart phone, acomputer, a tablet, a smart plug, etc. The Type 1 device and Type 2devices may be placed in/near a conference room to count people. TheType 1 device and Type 2 devices may be in a well-being monitoringsystem for older adults to monitor their daily activities and any signof symptoms (e.g. dementia, Alzheimer's disease). The Type 1 device andType 2 device may be used in baby monitors to monitor the vital signs(breathing) of a living baby. The Type 1 device and Type 2 devices maybe placed in bedrooms to monitor quality of sleep and any sleep apnea.The Type 1 device and Type 2 devices may be placed in cars to monitorwell-being of passengers and driver, detect any sleeping of driver anddetect any babies left in a car. The Type 1 device and Type 2 devicesmay be used in logistics to prevent human trafficking by monitoring anyhuman hidden in trucks and containers. The Type 1 device and Type 2devices may be deployed by emergency service at disaster area to searchfor trapped victims in debris. The Type 1 device and Type 2 devices maybe deployed in an area to detect breathing of any intruders. There arenumerous applications of wireless breathing monitoring withoutwearables.

Hardware modules may be constructed to contain either the Type 1transceiver and the Type 2 transceiver. The hardware modules may be soldto/used by variable brands to design, build and sell final commercialproducts. Products using the disclosed system and/or method may behome/office security products, sleep monitoring products, WiFi products,mesh products, TV, STB, entertainment system, HiFi, speaker, homeappliance, lamps, stoves, oven, microwave oven, table, chair, bed,shelves, tools, utensils, torches, vacuum cleaner, smoke detector, sofa,piano, fan, door, window, door/window handle, locks, smoke detectors,car accessories, computing devices, office devices, air conditioner,heater, pipes, connectors, surveillance camera, access point, computingdevices, mobile devices, LTE devices, 3G/4G/5G/6G devices, gamingdevices, eyeglasses, glass panels, VR goggles, necklace, watch, waistband, belt, wallet, pen, hat, wearables, implantable device, tags,parking tickets, smart phones, etc.

In addition, sleep monitoring acts as a critical and challenging taskthat attracts increasing demands and interests. The present teachingdiscloses the model, design, and implementation of SMARS (SleepMonitoring via Ambient Radio Signals), which is the first practicalSleep Monitoring system that exploits Ambient Radio Signals to recognizesleep stages and assess sleep quality. This will enable a future smarthome that monitors daily sleep in a ubiquitous, non-invasive andcontactless manner, without instrumenting the subject's body or the bed.Different from previous RF-based approaches, the present teachingdevises a statistical model that accounts for all reflecting andscattering multipaths, allowing highly accurate and instantaneousbreathing estimation with ever best performance achieved on commoditydevices. On this basis, SMARS then recognizes different sleep stages,including wake, REM, and NREM, which is previously only possible withdedicated hardware. A real-time system is implemented on commercial WiFichipsets and deployed in 6 homes with 6 participants, resulting in 32nights of data in total. The results demonstrate that SMARS yields amedian error of 0.47 bpm and a 95% error of only 2.92 bpm for breathingestimation, and detects breathing robustly even when a person is 10 maway from the link, or behind a wall. SMARS achieves sleep stagingaccuracy of 85%, outperforming advanced solutions using contact sensoror radar. Furthermore, SMARS is evaluated upon a recently releaseddataset that measures 20 patients' overnight sleep, which confirms theperformance. By achieving promising results with merely a singlecommodity RF link, SMARS will set the stage for a practical in-homesleep monitoring solution.

Sleep plays a vital role in an individual's health and well-being, bothmentally and physically. It is well recognized that sleep quantity andquality is fundamentally related to health risks like cardiovasculardecease, stroke, kidney failure, diabetes, and adverse mentalconditions, etc. Unfortunately, in modern society, a number of peoplesuffer from sleep disorders. As recently reported, 10% of the populationsuffers from chronic insomnia (which is even higher among elders), and ⅓of Americans do not get sufficient sleep. Monitoring sleep emerges as anessential demand to help, manage, diagnose, and treat the growing groupof sleep disorders as well as to keep regular tabs on personal health.

Sleep monitoring, however, is a challenging task that has drawntremendous efforts for decades. Generally, it measures sleep time,recognizes different sleep stages, e.g., wake, REM (Rapid Eye Movement)and NREM (Non-REM), and accordingly assesses an individual's sleepquality. Various solutions have been proposed. The medical gold standardrelies on Polysomnography (PSG), which monitors various physiologicalparameters such as brain activities, respirations, and body movements bya number of wired sensors attached to the patient. Albeit accurate andcomprehensive, PSG is usually expensive and cumbersome with the invasivesensors that may cause sleep difficulties, limiting itself to clinicalusage for confirmed patients. Other approaches includingphotoplethysmography (PPG) and actigraphy (ACT) require users to weardedicated sensors during sleep. Ballistocardiogram (BCG) needs toinstrument the mattress with an array of EMFi sensors to measureballistic force. Despite of the costs, these approaches provide suitablesolutions for those who need special cares but are less-than-ideal forthe public. Recent efforts in mobile computing envision in-home sleepmonitoring using smartphones and wearables. These methods, however, onlyprovide coarse-grained, less accurate measurements and fail to monitorvital signs like respiratory rate. In addition, mobiles and wearablesare undesirable for especially elders and those with dementia.

Different from prevailing solutions, the disclosed solution looksforward to a future smart home that monitors daily sleep in aubiquitous, non-invasive, contactless, and accurate manner, withoutinstrumenting the body or the bed. One can observe an opportunitytowards such a system by two perspectives: 1) Clinical study has shownthat physiological activity varies among different sleep stages. Forexample, breathing rate becomes irregular and fast since brain oxygenconsumption increases during REM sleep, and is more stable and slowerduring NREM sleep, rendering the feasibility of sleep staging based onbreathing monitoring. 2) Recent advances in wireless technology havedemonstrated non-contact sensing of body motions in the environments.Chest and abdomen motions caused by breathing can alter radio signalpropagations and thus modulate the received signals, from which it isthen possible to decipher breathing. One can explore a synergy betweenthe two perspectives, resulting in a system to leverage ambient radiosignals (e.g., WiFi) to capture a person's breathing and motion duringsleep and further monitor the sleep behaviors.

While early works have investigated the feasibility of RF-basedbreathing estimation and sleep monitoring, they either rely onspecialized hardware like FMCW radar, or only work in controlledenvironments. Solutions based on dedicated radios are usually expensiveand not ubiquitously applicable. Others using commodity devicestypically require the user to lay still on a bed with radios exceedinglyclose to his/her chest and fail in presence of extraneous motions or inNon-Line-Of-Sight (NLOS) scenarios. In addition, none of them canidentify different sleep stages due to their limited accuracy inbreathing estimation. Such limitations prevent them from application forpractical in-home sleep monitoring.

The present teaching disclose the model, design, and implementation ofSMARS, the first practical Sleep Monitoring system that exploitscommodity Ambient Radio Signals to recognize sleep stages and assess theotherwise elusive sleep quality. SMARS works in a non-obtrusive waywithout any body contact. All that a user needs to do is to set up onesingle link between two commodity radios by, e.g., simply placing areceiver if a wireless router is already installed inside the home.SMARS advances the literature by a novel statistical model that allowshighly accurate and instantaneous breathing estimation. On this basis,SMARS is able to distinguish different sleep stages, which is previouslyonly obtainable by expensive dedicated hardware. Specifically, SMARSexcels in three unique aspects to deliver practical sleep monitoring.First, one can devise a statistical model on motion in Channel StateInformation (CSI) that leverages all reflection and scatteringmultipaths indoors. Existing works usually assume a geometrical modelwith a few multipaths and one major path reflected off the human body(e.g., using a 2-ray model developed for outdoor space). Underreal-world indoor environments, however, there could be as many ashundreds of multipaths, and signals not only reflect but also scatteroff a person's body and other objects in the space. As a consequence,previous approaches fail in NLOS environments and for minute motions dueto the lack of a dominant reflection path. In contrast, the disclosedmodel investigates the statistical characteristics of motion in CSIwithout making such unrealistic assumptions, underlying robust detectionof arbitrary motions including breathing.

Second, SMARS achieves accurate respiratory rate estimationinstantaneously and robustly. Most of previous breathing estimationschemes assume constant breathing rate during a relatively large timewindow to gain sufficient frequency resolution, losing fine-grainedbreathing variations during sleep. In addition, minute breathing motionscan be easily buried in CSI measurement noises, rendering existingphilosophies effective only in extraordinary close proximity (typicallywithin 2-3 m) without any extraneous motions. To improve timeresolution, SMARS exploits the time-domain Auto-Correlation Function(ACF) to estimate breathing rate, which can report real-time breathingrates as frequent as every one second and make it possible to captureinstantaneous breathing rate changes. By using ACF, SMARS alsocircumvents the use of noisy phase and the usually handcrafted CSIdenoising procedure. More importantly, by eliminating the frequencyoffsets and thus synchronizing breathing signal over differentsubcarriers, ACF allows us to perform Maximal Ratio Combining (MRC) tocombine multiple subcarriers to combat measurement noises and maximizebreathing signals in an optimal way. By doing so, one can push the limitof the breathing signal to noise ratio (SNR) and thus significantlyincrease the sensing sensitivity for larger coverage as well as weakerbreathing. Specifically, SMARS can reliably detect breathing when aperson is 10 m away from the link, or behind a wall, which is evenbetter than specialized low-power radars.

Finally, based on the extracted breathing rates and motion statisticsduring sleep, one can recognize different sleep stages (including wake,REM and NREM) and comprehensively assess the overall sleep quantity andquality. Based on in-depth understanding of the relationship betweenbreathing rates and sleep stages, one can extract distinctive breathingfeatures for classification for sleep staging. None of existing worksusing off-the-shelf devices can achieve the same goal of staging sleep.

A real-time system has been implemented on different commercial WiFichipsets and its performance is evaluated through extensive experiments.The evaluation includes two parts: 1) One can deploy SMARS in 6 homeswith 6 healthy subjects and collect 32 nights of data, 5 out of whichhave PSG data recorded by commercial devices. The results show thatSMARS achieves great performance, with a median breathing estimationerror of 0.47 breath per minute (bpm) and a 95% tile error of 2.92 bmp.Regarding sleep staging, SMARS produces a remarkable accuracy of 85.2%,while commercial solutions, e.g., EMFIT based on contact sensors andResMed using radar, have accuracies of only 69.8% and 83.7%respectively. 2) One can further validate SMARS on a recently releaseddataset on RF-based respiration monitoring. The dataset collected 20patients' overnight sleep for comparative evaluation of fourstate-of-the-art breathing monitoring systems, using clinically labeledPSG data as ground truths. All the four systems (based on ZigBee,Sub-RSS radio, UWB radar, and WiFi CSI, respectively) producesignificant median errors of about 2-3 bpm and 95% tile errors of aroundor above 10 bpm. As comparison, SMARS achieves significant improvementsby decreasing the median error to 0.66 bpm and the 95% tile error to3.79 bpm. By achieving promising performance, SMARS can deliverclinically meaningful sleep monitoring for daily and regular use inpractice and takes an important step towards a future smart home thatmonitors personal health everyday life.

In a nutshell, the core contribution here is SMARS, the first systemthat enables a smart home to stage an inhabitant's sleep using commodityoff-the-shelf WiFi devices, by achieving highly accurate andinstantaneous breathing estimation in the wild. SMARS also contributesthe first statistical model for understanding and capturing motions inCSI, which will renovate various applications in wireless sensing.

In one embodiment, the present teaching discloses a method, anapparatus, a device, a system, and/or software(method/apparatus/device/system/software) of a wireless monitoringsystem. A time series of channel information (CI) of a wirelessmultipath channel may be obtained using a processor, a memorycommunicatively coupled with the processor and a set of instructionsstored in the memory. The time series of CI may be extracted from awireless signal transmitted between a Type 1 heterogeneous wirelessdevice and a Type 2 heterogeneous wireless device in a venue through thewireless multipath channel. The wireless multipath channel may beimpacted by a motion of an object in the venue. A characteristics and/ora spatial-temporal information of the object and/or of the motion of anobject may be monitored based on the time series of CI. A task may beperformed based on the characteristics and/or the spatial-temporalinformation. A presentation associated with the task may be generated ina user-interface (UI) on a device of a user. The time series of CI maybe preprocessed.

The Type 1 device may comprise at least one heterogeneous wirelesstransmitter. The Type 2 device may comprise at least one heterogeneouswireless receiver. The Type 1 device and the Type 2 device may be thesame device. Any device may have a processor, a memory communicativelycoupled with the processor, and a set of instructions stored in thememory to be executed by the processor.

There may be multiple Type 1 heterogeneous wireless devices interactingwith the same Type 2 heterogeneous wireless device, and/or there may bemultiple Type 2 heterogeneous wireless devices interacting with the sameType 1 heterogeneous wireless device. The multiple Type 1 devices/Type 2devices may be synchronized and/or asynchronous, with same/differentwindow width/size and/or time shift. The multiple Type 1 devices/Type 2devices may operate independently and/or collaboratively. The multipleType 1 devices/Type 2 devices may be communicatively coupled to same ordifferent servers (e.g. cloud server, edge server, local server).Operation of one device may be based on operation, state, internalstate, storage, processor, memory output, physical location, computingresources, network of another device. Difference devices may communicatedirectly, and/or via another device/server/cloud server. The devices maybe associated with one or more users, with associated settings. Thesettings may be chosen once, pre-programmed, and/or changed over time.

In the case of multiple Type 1 devices interacting with multiple Type 2devices, any processing (e.g. time domain processing and/or frequencydomain processing) may be different for different devices. Theprocessing may be based on locations, orientation, direction, roles,user-related characteristics, settings, configurations, availableresources, available bandwidth, network connection, hardware, software,processor, co-processor, memory, battery life, available power,antennas, antenna types, directional/unidirectional characteristics ofthe antenna, power setting, and/or other parameters/characteristics ofthe devices.

The wireless receiver (e.g. Type 2 device) may receive the wirelesssignal and/or another wireless signal from the wireless transmitter(e.g. Type 1 device). The wireless receiver may receive another wirelesssignal from another wireless transmitter (e.g. a second Type 1 device).The wireless transmitter may transmit the wireless signal and/or anotherwireless signal to another wireless receiver (e.g. a second Type 2device). The wireless transmitter, the wireless receiver, the anotherwireless receiver and/or the another wireless transmitter may be movingwith the object and/or another object. The another object may betracked.

The Type 1 device may be capable of wirelessly coupling with at leasttwo Type 2 devices. The Type 2 device may be capable of wirelesslycoupling with at least two Type 1 devices. The Type 1 device may becaused to switch/establish wireless coupling from the Type 2 device to asecond Type 2 heterogeneous wireless device at another location in thevenue. Similarly, the Type 2 device may be caused to switch/establishwireless coupling from the Type 1 device to a second Type 1heterogeneous wireless device at yet another location in the venue. Theswitching may be controlled by a server, the processor, the Type 1device, the Type 2 device, and/or another device. The radio used beforeswitching may be different from the radio used after the switching. Asecond wireless signal may be caused to be transmitted between the Type1 device and the second Type 2 device (or between the Type 2 device andthe second Type 1 device) through the wireless multipath channel. Asecond time series of CI of the wireless multipath channel extractedfrom the second wireless signal may be obtained. The second wirelesssignal may be the first wireless signal. The characteristics, thespatial-temporal information and/or another quantity of the motion ofthe object may be monitored based on the second time series of CI. TheType 1 device and the Type 2 device may be the same device.

The wireless signal and/or the another wireless signal may have dataembedded. The wireless receiver, the wireless transmitter, the anotherwireless receiver and/or the another wireless transmitter may beassociated with at least one processor, memory communicatively coupledwith respective processor, and/or respective set of instructions storedin the memory which when executed cause the processor to perform anyand/or all steps needed to determine the spatial-temporal information,the initial spatial-temporal information, the initial time, thedirection, the instantaneous location, the instantaneous angle, and/orthe speed, of the object.

The processor, the memory and/or the set of instructions may beassociated with the Type 1 heterogeneous wireless transceiver, one ofthe at least one Type 2 heterogeneous wireless transceiver, the object,a device associated with the object, another device associated with thevenue, a cloud server, and/or another server.

The Type 1 device may transmit the wireless signal (e.g. a series ofprobe signals) in a broadcasting manner to at least one Type 2 device(s)through the wireless multipath channel in the venue. The wireless signalis transmitted without the Type 1 device establishing wirelessconnection with any of the at least one Type 2 device receiver(s).

The Type 1 device may transmit to a particular destination media accesscontrol (MAC) address common for more than one Type 2 devices. Each ofthe more than one Type 2 devices may adjust their MAC address to theparticular MAC address.

The particular destination MAC address (common destination MAC address)may be associated with the venue. The association may be recorded in anassociation table of an Association Server. The venue may be identifiedby the Type 1 device, a Type 2 device and/or another device based on theparticular destination MAC address, the series of probe signals, and/orthe at least one time series of CI extracted from the probe signals.

For example, a Type 2 device may be moved to a new location in the venue(e.g. from another venue). And the Type 1 device may be newly set up inthe venue such that the Type 1 device and the Type 2 device are notaware of the existence of each other, and they may not establishwireless connection at all. During set up, the Type 1 device may beinstructed/guided/caused/controlled to send the series of probe signalsto the particular destination MAC address (e.g. using a dummy receiver,using a hardware pin setting/connection, using a stored setting, using alocal setting, using a remote setting, using a downloaded setting, orusing a server). Upon power up, the Type 2 device may scan for probesignals according to a table of destination MAC addresses that may beused at different locations (e.g. different MAC address for differentvenue such as house, office, enclosure, floor, multi-storey building,store, airport, mall, stadium, hall, station, subway, lot, area, zone,region, district, city, country, continent). When the Type 2 devicedetects the probe signals sent to the particular destination MACaddress, the Type 2 device can use the table to identify the venue basedon the particular destination MAC address.

A location of a Type 2 device in the venue may be computed based on theparticular destination MAC address, the series of probe signals, and/orthe at least one time series of CI obtained by the Type 2 device fromthe probe signals. The computing may be performed by the Type 2 device.

The particular destination MAC address may be changed over time. It maybe changed according to a time table, a rule, a policy, a mode, acondition, a situation and/or a change. The particular destination MACaddress may be selected based on availability of the MAC address, apre-selected list, collision pattern, traffic pattern, data trafficbetween the Type 1 device and another device, effective bandwidth,random selection, and/or a MAC address switching plan. The particulardestination MAC address may be the MAC address of a second wirelessdevice (e.g. a dummy receiver, or a receiver that serves as a dummyreceiver).

The Type 1 device may transmit the probe signals in a channel selectedfrom a set of channels. At least one CI of the selected channel may beobtained by a respective Type 2 device from the probe signal transmittedin the selected channel.

The selected channel may be changed over time. The change may beaccording to a time table, a rule, a policy, a mode, a condition, asituation, and/or a change. The selected channel may be selected basedon availability of channels, a pre-selected list, co-channelinterference, inter-channel interference, channel traffic patterns, datatraffic between the Type 1 device and another device, effectivebandwidth associated with channels, a security criterion, randomselection, a channel switching plan, a criterion, and/or aconsideration.

The particular destination MAC address and/or an information of theselected channel may be communicated between the Type 1 device and aserver through a network. The particular destination MAC address and/orthe information of the selected channel may also be communicated betweena Type 2 device and a server through another network. The Type 2 devicemay communicate the particular destination MAC address and/or theinformation of the selected channel to another Type 2 device (e.g. via amesh network, via Bluetooth, via WiFi, etc). The particular destinationMAC address and/or the information of the selected channel may be chosenby a server. The particular destination MAC address and/or theinformation of the selected channel may be signaled in an announcementchannel by the Type 1 device and/or a server. Before the particulardestination MAC address and/or the information of the selected channelis being communicated, it may be pre-processed.

Wireless connection between the Type 1 device and another wirelessdevice may be established. The Type 1 device may send a first wirelesssignal (e.g. a sounding frame, a probe signal, a request-to-send RTS) tothe another wireless device. The another wireless device may reply bysending a second wireless signal (e.g. a command, or a clear-to-sendCTS) to the Type 1 device, triggering the Type 1 device to transmit thewireless signal (e.g. series of probe signals) in the broadcastingmanner to the at least one Type 2 device without establishing wirelessconnection with any of the at least one Type 2 device. The secondwireless signal may be a response or an acknowledge (e.g. ACK) to thefirst wireless signal. The second wireless signal may contain a datawith information of at least one of: the venue, and the Type 1 device.The first wireless signal may also be in response to the second wirelesssignal.

The another wireless device may be a dummy wireless device with apurpose (e.g. a primary purpose, a secondary purpose) to establish thewireless connection with the Type 1 device, to receive the firstwireless signal, and/or to send the second wireless signal. The anotherwireless device may be physically attached to the Type 1 device.

In another example, the another wireless device may send a thirdwireless signal to the Type 1 device triggering the Type 1 device totransmit the wireless signal (e.g. series of probe signals) in thebroadcasting manner to the at least one Type 2 device withoutestablishing wireless connection with any of the at least one Type 2device. The Type 1 device may reply to the third wireless signal bytransmitting a fourth wireless signal to the another wireless device.

The another wireless device may not communicate further with the Type 1device after establishing the connection with the Type 1 device. Theanother wireless device may suspend communication with the Type 1 deviceafter establishing the connection. The communication may be resumed inthe future. The another wireless device may enter an inactive mode, ahibernation mode, a sleep mode, a stand-by mode, a low-power mode, anOFF mode and/or a power-down mode, after establishing the wirelessconnection with the Type 1 device.

The another wireless device may have the particular destination MACaddress. It may use the wireless connection to trigger the Type 1 deviceto send the at least one series of probe signals in a broadcastingmanner to the particular destination MAC address. Each of the at leastone Type 2 device may set its MAC address to the particular destinationMAC address so as to receive the at least one series of probe signalsfrom the Type 1 device. A new Type 2 device in the venue may obtain theparticular destination MAC address from a designated source (e.g. acloud server, an edge server, a remote server, a local server, a webserver, an internet server, a webpage, a database) and set its MACaddress to the particular destination MAC address so as to receive theat least one series of probe signals from the Type 1 device

Both the Type 1 device and the another wireless device may be controlledand/or coordinated by at least one of: a first processor associated withthe Type 1 device, a second processor associated with the anotherwireless device, a third processor associated with the designated sourceand/or a fourth processor associated with another device. The firstprocessor and the second processor may coordinate with each other.

A first series of probe signals may be transmitted by a first antenna ofthe Type 1 device to at least one first Type 2 device through a firstwireless multipath channel in a first venue. A second series of probesignals may be transmitted by a second antenna of the Type 1 device toat least one second Type 2 device through a second wireless multipathchannel in a second venue. The first series and the second seriesmay/may not be different. The at least one first Type 2 device may/maynot be different from the at least one second Type 2 device. The firstseries and/or the second series of probe signals may be transmitted in abroadcasting manner. The first antenna may/may not be different from thesecond antenna.

The Type 1 device may send the two separate series of probe signals totwo separate groups of Type 2 devices located in two venues. The firstset of Type 2 devices (the at least one first Type 2 device) may receivethe first time series of probe signals through a first wirelessmultipath channel of the first venue. The second set of Type 2 devices(the at least one second Type 2 device) may receive the second timeseries of probe signals through a second wireless multipath channel inthe second venue. If in broadcasting mode, Type 1 device may notestablish wireless connection with any of the at least one first andsecond Type 2 devices.

The two venues may have different sizes, shape, multipathcharacteristics. The first venue and the second venue may overlappartially. The immediate area around the first antenna and secondantenna may be part of the overlapped area. The first venue may be asubset of the second venue, or vice versa. The first wireless multipathchannel and the second wireless multipath channel may be different. Forexample, the first one may be WiFi while the second may be LTE. Or, bothmay be WiFi, but the first one may be 2.4 GHz WiFi and the second may be5 GHz WiFi. Or, both may be 2.4 GHz WiFi, but may have different channelnumbers, with different SSID names, and WiFi settings (e.g.encryption—the first may use PKA and the second may use PKA2).

Each first Type 2 device may obtain at least one first time series of CIfrom the first series of probe signals, the CI being of the firstwireless multipath channel between the first Type 2 device and the Type1 device. Each second Type 2 device may obtain at least one second timeseries of CI from the second series of probe signals, the CI being ofthe second wireless multipath channel between the second Type 2 deviceand the Type 1 device.

The first antenna may/may not be the second antenna. The at least onefirst Type 2 device may/may not be the same as the at least one secondType 2 device. A particular first Type 2 device may be the same as aparticular second Type 2 device. The first and second series of probesignals may/may not be synchronous. The transmission of the first seriesof probe signals may not be synchronous to the transmission of thesecond series of probe signals. A probe signal may be transmitted withdata. A probe signal may be replaced by a data signal.

The first series of probe signals may be transmitted at a firstpseudo-regular interval (e.g. at a rate of 30 Hz). The second series ofprobe signals may be transmitted at a second pseudo-regular interval(e.g. at a rate of 300 Hz). The first pseudo-regular interval may/maynot be different from the second pseudo-regular interval. The firstand/or second pseudo-regular interval may be changed over time. Thechange may be according to at least one of: a time table, a rule, apolicy, a mode, a condition, a situation, and/or a change. Anypseudo-regular interval may be changed over time.

The first series of probe signals may be transmitted to a firstdestination MAC address. The second series of probe signals may betransmitted to a second destination MAC address. The two destination MACaddresses may/may not be different. Any of the two destination MACaddresses may be changed over time. The change may be according to atleast one of: a time table, a rule, a policy, a mode, a condition, asituation, and/or a change.

The first series of probe signals may be transmitted in a first channel.The second series of probe signals may be transmitted in a secondchannel. The first channel and the second channel may/may not bedifferent. The first channel and/or the second channel may be changedover time. The change may be according to at least one of: a time table,a rule, a policy, a mode, a condition, a situation, and/or a change.

A first wireless connection may be established between the Type 1 deviceand another wireless device. The Type 1 device may establish the firstwireless connection with the another wireless device. The Type 1 devicemay send a first wireless signal to the another wireless device. Theanother wireless device may send a second wireless signal to the Type 1device. One of the first wireless signal and the second wireless signalmay be in response to the other. The second wireless signal to the Type1 device may trigger the Type 1 device to transmit the first series ofprobe signals in the broadcasting manner. The second wireless signal maybe an acknowledge to the first wireless signal.

A second wireless connection may be established between the Type 1device and yet another wireless device. The Type 1 device may establishthe second wireless connection with the yet another wireless device. TheType 1 device may send a third wireless signal to the yet anotherwireless device. The yet another wireless device may send a fourthwireless signal to the Type 1 device. One of the third wireless signaland the fourth wireless signal may be in response to the other. Thefourth wireless signal to the Type 1 device may trigger the Type 1device to transmit the second series of probe signals in thebroadcasting manner. The fourth wireless signal may be an acknowledge tothe third wireless signal. The third wireless signal may be similar tothe first wireless signal. The fourth wireless signal may be similar tothe second wireless signal. The yet another wireless device may be theanother wireless device. The third wireless signal may be sent by theType 1 device before the first wireless communication is fullyestablished and/or completed.

The another wireless device and the yet another wireless device may bethe same device, which may perform signal handshake (e.g. the handshakewith the first wireless signal and second wireless signal, or thehandshake with the third signal and the fourth signal) with all Type 1devices sequentially to trigger them to transmit in the broadcastingmanner. The signal handshake with the Type 1 devices may be performed inat least one of: a sequential manner, a parallel manner and a mixedmanner (partially sequential, partially parallel).

The Type 1 device, the another wireless device and/or the yet anotherwireless device may be controlled and/or coordinated, physicallyattached, or may be of/in/of a common device. At least two of the Type 1device, the another wireless device and/or the yet another wirelessdevice may be controlled by/connected to a common data processor, or maybe connected to a common bus interconnect/network/LAN/Bluetoothnetwork/BLE network/wired network/wireless network/mesh network/mobilenetwork/cloud, or may share a common memory, or may be associated with acommon user/user profile/user account/identity(ID)/household/house/physical address/location/geographic coordinate/IPsubnet/SSID/user device/home device/office device/manufacturing device.

The Type 1 device, the another wireless device and/or the yet anotherwireless device may each be associated a processor, a memorycommunicatively coupled with the processor, and a set of instructionstored in the memory to be executed by the processor. The Type 1 device,the another wireless device and/or the yet another wireless device mayeach comprise a processor, a memory communicatively coupled with theprocessor, and a set of instruction stored in the memory to be executedby the processor. When executed, the set of instructions may cause thedevice to perform an operation. Their processors may be coordinated.

At least one series of probe signals may be transmitted asynchronouslyand contemporaneously by each of more than one Type 1 heterogeneouswireless devices. Each respective series of probe signals is transmittedby an antenna of respective Type 1 heterogeneous wireless device in abroadcasting manner to a respective set of Type 2 devices through awireless multipath channel, without the respective Type 1 deviceestablishing wireless connection with any of the respective set of Type2 receivers.

The transmitting may be using a respective processor, a respectivememory and a respective set of instructions. Each of the more than oneType 1 heterogeneous wireless devices may have a heterogeneousintegrated circuit (IC). Each of the respective set of Type 2 Type 2device may have a respective heterogeneous IC.

Each Type 1 device may be the signal source of a set of respective Type2 devices (i.e. the Type 1 device sends a respective wireless signal(series of probe signals) to the set of respective Type 2 devices. Eachrespective Type 2 device chooses the Type 1 device from among all Type 1devices as its signal source. Each Type 2 device may chooseasynchronously (at different time). At least one time series of CI maybe obtained by each respective Type 2 device from the respective seriesof probe signals from the Type 1 device, the CI being of the wirelessmultipath channel between the Type 2 device and the Type 1 device.

The respective Type 2 device chooses the Type 1 device from among allType 1 devices as its signal source based on at least one of: identity(ID) of the Type 1 device and/or the respective Type 2 receiver,information of at least one of: a user, an account, access info, aparameter, a characteristics, and a signal strength associated with theType 1 device and/or the respective Type 2 receiver, a task to beperformed by the Type 1 device and/or the respective Type 2 receiver,whether the Type 1 device is the same as a past signal source, a history(of the past signal source, the Type 1 device, another Type 1 device,the respective Type 2 receiver, and another Type 2 receiver), and/or atleast one threshold for switching signal source.

Initially, the Type 1 device may be the signal source of a set ofinitial respective Type 2 devices (i.e. the Type 1 device sends arespective wireless signal (series of probe signals) to the set ofinitial respective Type 2 devices) at an initial time. Each initialrespective Type 2 device chooses the Type 1 device from among all Type 1devices as its signal source.

The signal source (Type 1 device) of a particular Type 2 device may bechanged when at least one of the following occurs: (1) a time intervalbetween two adjacent probe signals (e.g. between the current probesignal and an immediate past probe signal, or between the next probesignal and the current probe signal) received from the current signalsource of the particular Type 2 device exceeds a first threshold, (2) asignal strength associated with the current signal source of the Type 2device is below a second threshold at the respective current time, (3) aprocessed signal strength associated with the current signal source ofthe Type 2 device is below a second threshold at the respective currenttime after the signal strength is processed with at least one of: a lowpass filter, a band pass filter, a median filter, a moving averagefilter, a weighted averaging filter, a linear filter and a non-linearfilter, and/or (4) the signal strength (or processed signal strength)associated with the current signal source of the Type 2 device is belowa fourth threshold for a significant percentage of a recent time window(e.g. 70%, 80%, 90%, etc.). The percentage may exceed a fifth threshold.Any of first threshold, second threshold, third threshold, fourththreshold and fifth threshold may be time varying.

Condition (1) may occur when the Type 1 device and the Type 2 devicebecome progressively far away from each other, such that some probesignal from the Type 1 device becomes too weak and is not received bythe Type 2 device. Conditions (2)-(4) may occur when the two devicesbecome far from each other such that the signal strength becomes veryweak.

The signal source of the particular Type 2 device may not change if theother Type 1 devices have signal strength weaker than the current signalsource (current selected Type 1 device). In another example, the signalsource of the particular Type 2 device may also not change if the otherType 1 devices have signal strength weaker than a factor (e.g. 1.1, 1.2,or 1.5, etc.) of signal strength of the current signal source.

If the signal source is changed, the new signal source may take effectat a near future time (e.g. the respective next time). The new signalsource may be the Type 1 device with strongest signal strength. The newsignal source may also be the Type 1 device with strongest processedsignal strength, wherein the signal strength of each Type 1 device maybe processed with at least one of: a low pass filter, a band passfilter, a median filter, a moving average filter, a weighted averagingfilter, a linear filter and a non-linear filter.

A list of available Type 1 devices may be initialized. The list may beupdated by examining signal strength associated with the respective setof Type 1 devices. The list of available Type 1 devices may also beupdated by examining processed signal strength associated with therespective set of Type 1 devices.

The respective chosen Type 1 device may/may not be different from thecurrent signal source.

A Type 2 device may choose between the first series of probe signals andthe second series of probe signals based on: respective pseudo-regularintervals, respective destination MAC addresses, respective channels,respective characteristics/properties/states, a respective task to beperformed by the Type 2 device, signal strength of first series andsecond series, and/or another consideration.

The yet another wireless device and the another wireless device may bethe same device. They may be in/on/of a common device. The Type 1 devicemay first establish the first wireless communication with the anotherwireless device, and then establish the second wireless communicationwith the yet another wireless device. The Type 1 device may startestablishing the second wireless communication with the yet anotherwireless device before the first wireless communication is fullyestablished. The Type 1 device may start establishing the secondwireless communication with the yet another wireless device while thefirst wireless communication is being established.

The another wireless device may set its MAC address to a firstdestination MAC address and establish the first wireless connection withthe Type 1 device using the first destination MAC address. The yetanother wireless device may set its MAC address to a second destinationMAC address and establish the second wireless connection with the Type 1device using the second destination MAC address.

The transmitting may be using a processor, a memory and a set ofinstructions. Each of the Type 1 device and/or at least one Type 2device may also have a heterogeneous IC. The heterogeneous IC maycomprise the processor, memory and the set of instructions. Theheterogeneous IC in different devices may be the same or different. EachType 2 device may extract at least one time series of CI of the wirelessmultipath channel between the Type 1 device and the Type 2 device fromthe wireless signal. Each time series of CI is associated with anantenna of the Type 1 device and an antenna of the Type 2 device. Theheterogeneous IC may generate the wireless signal, transmit or receivethe wireless signal, extract the time series of CI from the wirelesssignal, and make the time series of CI available (e.g. to theprocessor/memory, other processors/memory).

Multiple Type 1 devices may transmit wireless signals asynchronously(e.g. to the same Type 2 device, to a number of Type 2 devices).

Multiple Type 2 devices may receive the wireless signal asynchronously.They may extract respective time series of CI from the wireless signalasynchronously, with respective heterogeneous starting time.

The wireless signal may be a series of probe signals. A probe signal maybe transmitted with data. A probe signal may be replaced by a datasignal.

The series of probe signals may be transmitted at a pseudo-regularinterval (e.g. 100 probe signals per second). The pseudo-regularinterval may be changed. The series of probe signals may be scheduled ata regular interval (e.g. 100 probe signals per second), but each probesignal may experience small time perturbation, perhaps due to timingrequirement, timing control, network control, handshaking, messagepassing, collision avoidance, carrier sensing, congestion, availabilityof resources, and/or another consideration.

The series of probe signals may be transmitted at the pseudo-regularinterval for a period of time. The pseudo-regular interval, the durationof the period of time and/or the starting time of the period of time,may be changed over time. For example, it may be 100 probe signals persecond (probe/sec in short) for 2 hours and then changed to 1000probe/sec for 30 minutes, and may change again.

The change may be according to a time table, a rule, a policy, a mode, acondition and/or a change (e.g. once every hour, or changed whenever acertain event occur). For example, the pseudo-regular interval maynormally be 100 probe/sec, but will be changed to 1000 probe/sec in somedemanding situations, and may be changed to 10 probe/sec in some lowpower and/or standby situation. The probe signals may be sent in burstalso, e.g. at 100 probe/sec for 1 minute and no signal for some timebefore and after the burst. Or, the probe signal may be sent at 100probe/sec and then momentarily at a burst rate of 1000 probe/sec for 1minute and then back to 100 probe/sec. In another example, the probesignal may be sent at 1 probe/sec in a low power mode, at 10 probe/secin a standby mode, at 100 probe/sec in a normal mode, and at 1000probe/sec in an armed mode, etc.

The change based on at least one task performed by Type 1 device or Type2 device. For example, the task of one receiver may need 10 probe/secwhile the task of another receiver may need 1000 probe/sec. Thepseudo-regular interval may be increased to 1000 probe/sec to satisfythe most demanding receiver. In one example, the receivers, the tasksassociated with the receivers and/or the wireless multipath channel asexperienced by the receivers may be divided into at least one class(e.g. a low priority class, a high priority class, an emergency class, acritical-task class, a regular class, a privileged class, anon-subscription class, a subscription class, a paying class, anon-paying class, etc.) and the pseudo-regular interval may be adjustedfor the sake of some selected class (e.g. the high priority class). Whenthe need of that selected class is less demanding, the pseudo-regularinterval may be changed (increased or decreased). When a receiver hascritically low power, the pseudo-regular interval may be reduced toreduce the power consumption of the receiver to respond to the probesignals.

In one example, the probe signals may be used to transfer powerwirelessly to a receiver, and the pseudo-regular interval may beadjusted to control the amount of power being wirelessly transferred tothe receiver.

The pseudo-regular interval may be changed by and/or based on a server,the Type 1 device and/or the Type 2 device. The server may becommunicatively coupled with the Type 1 device and/or the Type 2 device.The change may be communicated between the server, the Type 1 deviceand/or the Type 2 device. For example, the server may monitor, track,forecast and/or anticipate the needs of the Type 2 device and/or thetasks performed by the receivers, and may decide to change thepseudo-regular interval accordingly. The server may send the changerequest to the Type 1 device (immediately or with some time delay) andthe Type 1 device may then make the change. The server may keep track ofa time table and may make scheduled changes to the pseudo-regularinterval according to the time table. The server may detect an emergencysituation of a receiver and change the pseudo-regular intervalimmediately. The server may detect a slowly happening condition (and/oran evolving condition) associated with a receiver and gradually adjustthe pseudo-regular interval accordingly.

There may be more than one Type 1 devices. Each Type 1 device maytransmit a respective wireless signal to more than one respective Type 2devices through the wireless multipath channel in the venue. A CI timeseries may be obtained, each CI time series associated with a Type 1device and a Type 2 device.

The characteristics and/or the spatial-temporal information of themotion of the object may be monitored individually based on a CI timeseries associated with a particular Type 1 device and a particular Type2 device.

The characteristics and/or the spatial-temporal information of themotion of the object may be monitored jointly based on any CI timeseries associated with the particular Type 1 device and any Type 2device.

The characteristics and/or the spatial-temporal information of themotion of the object may also be monitored jointly based on any CI timeseries associated with the particular Type 2 device and any Type 1device.

The characteristics and/or the spatial-temporal information of themotion of the object may be monitored globally based on any CI timeseries associated with any Type 1 device and any Type 2 device.

Any joint monitoring (of the same motion or of the same object) may beassociated with at least one of: a user, a user account, a profile, ahousehold, a room, a location, and/or a user history, etc.

A first wireless multipath channel between a Type 1 device and a Type 2device may be different from a second wireless multipath channel betweenanother Type 1 device and another Type 2 device. The two wirelessmultipath channels may be associated with different frequency bands,different bandwidth, different carrier frequency, different modulation,different wireless standards, different coding, different encryption,different payload characteristics, different networks, different networkparameters, etc.

The first wireless multipath channel and the second wireless multipathchannel may be associated with different kinds of wireless system (e.g.two of the following: WiFi, LTE, LTE-A, 2.5G, 3G, 3.5G, 4G, beyond 4G,5G, 6G, 7G, a 802.11 system, a 802.15 system, a 802.16 system, meshnetwork, Zigbee, WiMax, Bluetooth, BLE, RFID, UWB, microwave system,radar like system, etc.). For example, the first may be WiFi while thesecond may be LTE.

The first wireless multipath channel and the second wireless multipathchannel may be associated with similar kinds of wireless system, but indifferent network. For example, the first wireless multipath channel maybe associated with a WiFi network named “Pizza and Pizza” in the 2.4 GHzband with a bandwidth of 20 MHz while the second may be associated witha WiFi network called “StarBud hotspot” in the 5 GHz band with abandwidth of 40 MHz.

The first wireless multipath channel may be associated with a particularwireless system (e.g. WiFi, or LTE, or 3G, or BLE, or mesh network, oradhoc network) while the second wireless multipath channel may beassociated another wireless system of same kind as the first wirelessmultipath channel (e.g. both WiFi, or both LTE, or both 3G, etc.) withdifferent network ID, SSID, characteristics, settings, and/orparameters. The first wireless multipath channel may also be a WiFichannel while the second wireless multipath channel may be another WiFichannel. For example, both first and second wireless multipath channelsmay be in the same WiFi network named “StarBud hotspot”, using twodifferent WiFi channels in “StarBud hotspot”.

In one embodiment, a wireless monitoring system may comprise training atleast one classifier of at least one known events in a venue based ontraining CI time series associated with the at least one events.

For each of the at least one known event happening in the venue in arespective training time period associated with the known event, arespective training wireless signal (e.g. a respective series oftraining probe signals) may be transmitted by an antenna of a first Type1 heterogeneous wireless device using a processor, a memory and a set ofinstructions of the first Type 1 device to at least one first Type 2heterogeneous wireless device through a wireless multipath channel inthe venue in the respective training time period.

At least one respective time series of training CI (training CI timeseries) may be obtained asynchronously by each of the at least one firstType 2 device from the (respective) training wireless signal. The CI maybe CI of the wireless multipath channel between the first Type 2 deviceand the first Type 1 device in the training time period associated withthe known event. The at least one training CI time series may bepreprocessed.

For a current event happening in the venue in a current time period, acurrent wireless signal (e.g. a series of current probe signals) may betransmitted by an antenna of a second Type 1 heterogeneous wirelessdevice using a processor, a memory and a set of instructions of thesecond Type 1 device to at least one second Type 2 heterogeneouswireless device through the wireless multipath channel in the venue inthe current time period associated with the current event.

At least one time series of current CI (current CI time series) may beobtained asynchronously by each of the at least one second Type 2 devicefrom the current wireless signal (e.g. the series of current probesignals). The CI may be CI of the wireless multipath channel between thesecond Type 2 device and the second Type 1 device in the current timeperiod associated with the current event. The at least one current CItime series may be preprocessed.

The at least one classifier may be applied to classify at least onecurrent CI time series obtained from the series of current probe signalsby the at least one second Type 2 device, to classify at least oneportion of a particular current CI time series, and/or to classify acombination of the at least one portion of the particular current CItime series and another portion of another CI time series. The at leastone classifier may also be applied to associate the current event with aknown event, an unknown event and/or another event.

Each wireless signal (e.g. each series of probe signals) may comprise atleast two CI each with an associated time stamp. Each CI may beassociated with a respective time stamp.

A current CI time series associated with a second Type 2 device andanother current CI time series associated with another second Type 2device may have different starting time, different time duration,different stopping time, different count of items in the time series,different sampling frequency, different sampling period between twoconsecutive items in the time series, and/or CI (CI) with differentfeatures.

The first Type 1 device and the second Type 1 device may be the samedevice. The first Type 1 device and the second Type 1 device may be atsame location in the venue.

The at least one first Type 2 device and the at least one second Type 2device may be the same. The at least one first Type 2 device may be apermutation of the at least one second Type 2 device. A particular firstType 2 device and a particular second Type 2 device may be the samedevice.

The at least one first Type 2 device and/or a subset of the at least onefirst Type 2 device may be a subset of the at least one second Type 2device. The at least one second Type 2 device and/or a subset of the atleast one second Type 2 device may be a subset of the at least one firstType 2 device.

The at least one first Type 2 device and/or a subset of the at least onefirst Type 2 device may be a permutation of a subset of the at least onesecond Type 2 device. The at least one second Type 2 device and/or asubset of the at least one second Type 2 device may be a permutation ofa subset of the at least one first Type 2 device.

The at least one second Type 2 device and/or a subset of the at leastone second Type 2 device may be at same respective location as a subsetof the at least one first Type 2 device. The at least one first Type 2device and/or a subset of the at least one first Type 2 device may be atsame respective location as a subset of the at least one second Type 2device.

The antenna of the Type 1 device and the antenna of the second Type 1device may be at same location in the venue. Antenna(s) of the at leastone second Type 2 device and/or antenna(s) of a subset of the at leastone second Type 2 device may be at same respective location asrespective antenna(s) of a subset of the at least one first Type 2device. Antenna(s) of the at least one first Type 2 device and/orantenna(s) of a subset of the at least one first Type 2 device may be atsame respective location(s) as respective antenna(s) of a subset of theat least one second Type 2 device.

A first section of a first time duration of the first CI time series anda second section of a second time duration of the second section of thesecond CI time series may be aligned. A map between items of the firstsection and items of the second section may be computed.

The first section may comprise at least one of: a first segment of thefirst CI time series with a first starting time and/or a first endingtime, and another segment of a processed first CI time series. Theprocessed first CI time series may be the first CI time series processedby a first operation.

The second section may comprise at least one of: a second segment of thesecond CI time series with a second starting time and a second endingtime, and another segment of a processed second CI time series. Theprocessed second CI time series may be the second CI time seriesprocessed by a second operation.

The first operation and/or the second operation may comprise at leastone of: subsampling, re-sampling, interpolation, filtering,transformation, feature extraction, pre-processing, and/or anotheroperation.

A first item of the first section may be mapped to a second item of thesecond section. The first item of the first section may also be mappedto another item of the second section. Another item of the first sectionmay also be mapped to the second item of the second section. The mappingmay be one-to-one, one-to-many, many-to-one, many-to-many.

At least one function of at least one of: the first item of the firstsection of the first CI time series, another item of the first CI timeseries, a time stamp of the first item, a time difference of the firstitem, a time differential of the first item, a neighboring time stamp ofthe first item, another time stamp associated with the first item, thesecond item of the second section of the second CI time series, anotheritem of the second CI time series, a time stamp of the second item, atime difference of the second item, a time differential of the seconditem, a neighboring time stamp of the second item, and another timestamp associated with the second item, may satisfy at least oneconstraint.

One constraint may be that a difference between the time stamp of thefirst item and the time stamp of the second item may be upper-bounded byan adaptive upper threshold and lower-bounded by an adaptive lowerthreshold.

The first section may be the entire first CI time series. The secondsection may be the entire second CI time series. The first time durationmay be equal to the second time duration.

A section of a time duration of a CI time series may be determinedadaptively. A tentative section of the CI time series may be computed. Astarting time and an ending time of a section (e.g. the tentativesection, the section) may be determined. The section may be determinedby removing a beginning portion and an ending portion of the tentativesection.

A beginning portion of a tentative section may be determined as follows.Iteratively, items of the tentative section with increasing time stampmay be considered as a current item, one item at a time.

In each iteration, at least one activity measure may be computed and/orconsidered. The at least one activity measure may be associated with atleast one of: the current item associated with a current time stamp,past items of the tentative section with time stamps not larger than thecurrent time stamp, and/or future items of the tentative section withtime stamps not smaller than the current time stamp. The current itemmay be added to the beginning portion of the tentative section if atleast one criterion associated with the at least one activity measure issatisfied.

The at least one criterion associated with the activity measure maycomprise at least one of: (a) the activity measure is smaller than anadaptive upper threshold, (b) the activity measure is larger than anadaptive lower threshold, (c) the activity measure is smaller than anadaptive upper threshold consecutively for at least a predeterminedamount of consecutive time stamps, (d) the activity measure is largerthan an adaptive lower threshold consecutively for at least anotherpredetermined amount of consecutive time stamps, (e) the activitymeasure is smaller than an adaptive upper threshold consecutively for atleast a predetermined percentage of the predetermined amount ofconsecutive time stamps, (f) the activity measure is larger than anadaptive lower threshold consecutively for at least anotherpredetermined percentage of the another predetermined amount ofconsecutive time stamps, (g) another activity measure associated withanother time stamp associated with the current time stamp is smallerthan another adaptive upper threshold and larger than another adaptivelower threshold, (h) at least one activity measure associated with atleast one respective time stamp associated with the current time stampis smaller than respective upper threshold and larger than respectivelower threshold, (i) percentage of time stamps with associated activitymeasure smaller than respective upper threshold and larger thanrespective lower threshold in a set of time stamps associated with thecurrent time stamp exceeds a threshold, and (j) another criterion.

An activity measure associated with an item at time T1 may comprise atleast one of: (1) a first function of the item at time T1 and an item attime T1−D1, wherein D1 is a pre-determined positive quantity (e.g. aconstant time offset), (2) a second function of the item at time T1 andan item at time T1+D1, (3) a third function of the item at time T1 andan item at time T2, wherein T2 is a pre-determined quantity (e.g. afixed initial reference time; T2 may be changed over time; T2 may beupdated periodically; T2 may be the beginning of a time period and T1may be a sliding time in the time period), and (4) a fourth function ofthe item at time T1 and another item.

At least one of: the first function, the second function, the thirdfunction, and/or the fourth function may be a function (e.g. F(X, Y, . .. )) with at least two arguments: X and Y.

The two arguments may be scalars. The function (e.g. F) may be afunction of at least one of: X, Y, (X−Y), (Y−X), abs(X−Y), X{circumflexover ( )}a, Y{circumflex over ( )}b, abs(X{circumflex over( )}a−Y{circumflex over ( )}b), (X−Y){circumflex over ( )}a, (X/Y),(X+a)/(Y+b), (X{circumflex over ( )}a/Y{circumflex over ( )}b), and((X/Y){circumflex over ( )}a−b), wherein a and b are may be somepredetermined quantities. For example, the function may simply beabs(X−Y), or (X−Y){circumflex over ( )}2, (X−Y){circumflex over ( )}4.The function may be a robust function. For example, the function may be(X−Y){circumflex over ( )}2 when abs (X−Y) is less than a threshold T,and (X−Y)+a when abs(X−Y) is larger than T. Alternatively, the functionmay be a constant when abs(X−Y) is larger than T. The function may alsobe bounded by a slowly increasing function when abs(X−y) is larger thanT, so that outliers cannot severely affect the result. Another exampleof the function may be (abs(X/Y)−a), where a=1. In this way, if X=Y(i.e. no change or no activity), the function will give a value of 0. IfX is larger than Y, (X/Y) will be larger than 1 (assuming X and Y arepositive) and the function will be positive. And if X is less than Y,(X/Y) will be smaller than 1 and the function will be negative.

In another example, both arguments X and Y may be n-tuples such thatX=(x_1, x_2, . . . , x_n) and Y=(y_1, y_2, . . . , y_n). The functionmay be a function of at least one of: x_i, y_i, (x_i−y_i), (y_i−x_i),abs(x_i−y_i), x_i{circumflex over ( )}a, y_i{circumflex over ( )}b,abs(x_i{circumflex over ( )}a−y_i{circumflex over ( )}b),(x_i−y_i){circumflex over ( )}a, (x_i/y_i), (x_i+a)/(y_i+b),(x_i{circumflex over ( )}a/y_i{circumflex over ( )}b), and((x_i/y_i){circumflex over ( )}a−b), wherein i is a component index ofthe n-tuple X and Y, and 1<=i<=n. E.g. component index of x_1 is i=1,component index of x_2 is i=2.

The function may comprise a component-by-component summation of anotherfunction of at least one of the following: x_i, y_i, (x_i−y_i),(y_i−x_i), abs(x_i−y_i), x_i{circumflex over ( )}a, y_i{circumflex over( )}b, abs(x_i{circumflex over ( )}a−y_i{circumflex over ( )}b),(x_i−y_i){circumflex over ( )}a, (x_i/y_i), (x_i+a)/(y_i+b),(x_i{circumflex over ( )}a/y_i{circumflex over ( )}b), and((x_i/y_i){circumflex over ( )}a−b), wherein i is the component index ofthe n-tuple X and Y. For example, the function may be in a form ofsum_{i=1}{circumflex over ( )}n (abs(x_i/y_i)−1)/n, orsum_{i=1}{circumflex over ( )}n w_i*(abs(x_i/y_i)−1), where w_i is someweight for component i.

The map may be computed using dynamic time warping (DTW). The DTW maycomprise a constraint on at least one of: the map, the items of thefirst CI time series, the items of the second CI time series, the firsttime duration, the second time duration, the first section, and/or thesecond section. Suppose in the map, the i{circumflex over ( )}{th}domain item is mapped to the j{circumflex over ( )}{th} range item. Theconstraint may be on admissible combination of i and j (constraint onrelationship between i and j).

Mismatch cost between a first section of a first time duration of afirst CI time series and a second section of a second time duration of asecond CI time series may be computed.

The first section and the second section may be aligned such that a mapcomprising more than one links may be established between first items ofthe first CI time series and second items of the second CI time series.With each link, one of the first items with a first time stamp may beassociated with one of the second items with a second time stamp.

A mismatch cost between the aligned first section and the aligned secondsection may be computed. The mismatch cost may comprise a function of:an item-wise cost between a first item and a second item associated by aparticular link of the map, and a link-wise cost associated with theparticular link of the map.

The aligned first section and the aligned second section may berepresented respectively as a first vector and a second vector of samevector length. The mismatch cost may comprise at least one of: an innerproduct, an inner-product-like quantity, a quantity based oncorrelation, a quantity based on covariance, a discriminating score, adistance, a Euclidean distance, an absolute distance, an Lk distance(e.g. L1, L2, . . . ), a weighted distance, a distance-like quantityand/or another similarity value, between the first vector and the secondvector. The mismatch cost may be normalized by the respective vectorlength.

A parameter derived from the mismatch cost between the first section ofthe first time duration of the first CI time series and the secondsection of the second time duration of the second CI time series may bemodeled with a statistical distribution. At least one of: a scaleparameter, a location parameter and/or another parameter, of thestatistical distribution may be estimated.

The first section of the first time duration of the first CI time seriesmay be a sliding section of the first CI time series. The second sectionof the second time duration of the second CI time series may be asliding section of the second CI time series.

A first sliding window may be applied to the first CI time series and acorresponding second sliding window may be applied to the second CI timeseries. The first sliding window of the first CI time series and thecorresponding second sliding window of the second CI time series may bealigned.

Mismatch cost between the aligned first sliding window of the first CItime series and the corresponding aligned second sliding window of thesecond CI time series may be computed. The current event may beassociated with at least one of: the known event, the unknown eventand/or the another event, based on the mismatch cost.

The classifier may be applied to at least one of: each first section ofthe first time duration of the first CI time series, and/or each secondsection of the second time duration of the second CI time series, toobtain at least one tentative classification results. Each tentativeclassification result may be associated with a respective first sectionand a respective second section.

The current event may be associated with at least one of: the knownevent, the unknown event and/or the another event, based on the mismatchcost.

The current event may be associated with at least one of: the knownevent, the unknown event and/or the another event, based on a largestnumber of tentative classification results in more than one sections ofthe first CI time series and corresponding more than sections of thesecond CI time series. For example, the current event may be associatedwith a particular known event if the mismatch cost points to theparticular known event for N consecutive times (e.g. N=10). In anotherexample, the current event may be associated with a particular knownevent if the percentage of mismatch cost within the immediate past Nconsecutive N pointing to the particular known event exceeds a certainthreshold (e.g. >80%).

In another example, the current event may be associated with a knownevent that achieves smallest mismatch cost for the most times within atime period. The current event may be associated with a known event thatachieves smallest overall mismatch cost, which is a weighted average ofat least one mismatch cost associated with the at least one firstsections. The current event may be associated with a particular knownevent that achieves smallest of another overall cost.

The current event may be associated with the “unknown event” if none ofthe known events achieve mismatch cost lower than a first threshold T1in a sufficient percentage of the at least one first section. Thecurrent event may also be associated with the “unknown event” if none ofthe events achieve an overall mismatch cost lower than a secondthreshold T2.

The current event may be associated with at least one of: the knownevent, the unknown event and/or the another event, based on the mismatchcost and additional mismatch cost associated with at least oneadditional section of the first CI time series and at least oneadditional section of the second CI time series.

The known events may comprise at least one of: a door closed event, adoor open event, a window closed event, a window open event, amulti-state event, an on-state event, an off-state event, anintermediate state event, a continuous state event, a discrete stateevent, a human-present event, a human-absent event, asign-of-life-present event, and/or a sign-of-life-absent event.

A projection for each CI may be trained using a dimension reductionmethod based on the training CI time series. The dimension reductionmethod may comprise at least one of: principal component analysis (PCA),PCA with different kernel, independent component analysis (ICA), Fisherlinear discriminant, vector quantization, supervised learning,unsupervised learning, self-organizing maps, auto-encoder, neuralnetwork, deep neural network, and/or another method. The projection maybe applied to at least one of: the training CI time series associatedwith the at least one event, and/or the current CI time series, for theat least one classifier.

The at least one classifier of the at least one event may be trainedbased on the projection and the training CI time series associated withthe at least one event. The at least one current CI time series may beclassified based on the projection and the current CI time series.

The projection may be re-trained using at least one of: the dimensionreduction method, and another dimension reduction method, based on atleast one of: the training CI time series, at least one current CI timeseries before retraining the projection, and/or additional training CItime series.

The another dimension reduction method may comprise at least one of:principal component analysis (PCA), PCA with different kernels,independent component analysis (ICA), Fisher linear discriminant, vectorquantization, supervised learning, unsupervised learning,self-organizing maps, auto-encoder, neural network, deep neural network,and/or yet another method,

The at least one classifier of the at least one event may be re-trainedbased on at least one of: the re-trained projection, the training CItime series associated with the at least one events, and/or at least onecurrent CI time series.

The at least one current CI time series may be classified based on: there-trained projection, the re-trained classifier, and/or the current CItime series.

Each CI may comprise a vector of complex values. Each complex value maybe preprocessed to give the magnitude of the complex value. Each CI maybe preprocessed to give a vector of non-negative real numbers comprisingthe magnitude of corresponding complex values.

Each training CI time series may be weighted in the training of theprojection.

The projection may comprise more than one projected components. Theprojection may comprise at least one most significant projectedcomponent. The projection may comprise at least one projected componentthat may be beneficial for the at least one classifier.

The channel information (CI) may be associated with/may comprise signalstrength, signal amplitude, signal phase, received signal strengthindicator (RSSI), channel state information (CSI), a channel impulseresponse (CIR), a channel frequency response (CFR). The CI may beassociated with information associated with a frequency band, afrequency signature, a frequency phase, a frequency amplitude, afrequency trend, a frequency characteristics, a frequency-likecharacteristics, a time domain element, a frequency domain element, atime-frequency domain element, an orthogonal decompositioncharacteristics, and/or a non-orthogonal decomposition characteristicsof the wireless signal through the wireless multipath channel.

The CI may also be associated with information associated with a timeperiod, a time signature, a time stamp, a time amplitude, a time phase,a time trend, and/or a time characteristics of the wireless signal. TheCI may be associated with information associated with a time-frequencypartition, a time-frequency signature, a time-frequency amplitude, atime-frequency phase, a time-frequency trend, and/or a time-frequencycharacteristics of the wireless signal. The CI may be associated with adecomposition of the wireless signal. The CI may be associated withinformation associated with a direction, an angle of arrival (AoA), anangle of a directional antenna, and/or a phase of the wireless signalthrough the wireless multipath channel. The CI may be associated withattenuation patterns of the wireless signal through the wirelessmultipath channel Each CI may be associated with a Type 1 device and aType 2 device. Each CI may be associated with an antenna of the Type 1device and an antenna of the Type 2 device.

The channel information (CI) may be obtained from a communicationhardware that is capable of providing the CI. The communication hardwaremay be a WiFi-capable chip/IC (integrated circuit), a chip compliantwith a 802.11 or 802.16 or another wireless/radio standard, a nextgeneration WiFi-capable chip, a LTE-capable chip, a 5G-capable chip, a6G/7G/8G-capable chip, a Bluetooth-enabled chip, a BLE (Bluetooth lowpower)-enabled chip, a UWB chip, another communication chip (e.g.Zigbee, WiMax, mesh network), etc. The communication hardware computesthe CI and stores the CI in a buffer memory and make the CI availablefor extraction. The CI may comprise data and/or at least one matricesrelated to channel state information (CSI). The at least one matricesmay be used for channel equalization, and/or beam forming, etc.

The wireless multipath channel may be associated with a venue. Theattenuation may be due to signal propagation in the venue, signalpropagating/reflection/refraction/diffraction through/at/around air(e.g. air of venue), refraction medium/reflection surface such as wall,doors, furniture, obstacles and/or barriers, etc. The attenuation may bedue to reflection at surfaces and obstacles (e.g. reflection surface,obstacle) such as floor, ceiling, furniture, fixtures, objects, people,pets, etc.

Each CI may be associated with a time stamp. Each CI may comprise N1components (e.g. N1 frequency domain components in CFR, N1 time domaincomponents in CIR, or N1 decomposition components). Each component maybe associated with a component index. Each component may be a realquantity, an imaginary quantity, a complex quantity, a magnitude, aphase, a flag, and/or a set. Each CI may comprise a vector of complexnumbers, a matrix of complex numbers, a set of mixed quantities, and/ora multi-dimensional collection of at least one complex numbers.

Components of a CI time series associated with a particular componentindex may form a respective component time series associated with therespective index. A CI time series may be divided into N1 component timeseries. Each respective component time series is associated with arespective component index. The characteristics/spatial-temporalinformation of the motion of the object may be monitored based on thecomponent time series.

A component-wise characteristics of a component-feature time series of aCI time series may be computed. The component-wise characteristics maybe a scalar (e.g. energy) or a function with a domain and a range (e.g.an autocorrelation function, a transform, an inverse transform). Thecharacteristics/spatial-temporal information of the motion of the objectmay be monitored based on the component-wise characteristics.

A total characteristics of the CI time series may be computed based onthe component-wise characteristics of each component time series of theCI time series. The total characteristics may be a weighted average ofthe component-wise characteristics. The characteristics/spatial-temporalinformation of the motion of the object may be monitored based on thetotal characteristics.

The Type 1 device and Type 2 device may support WiFi, WiMax, 3G/beyond3G, 4G/beyond 4G, LTE, 5G, 6G, 7G, Bluetooth, BLE, Zigbee, a proprietarywireless system, and/or another wireless system.

A common wireless system and/or a common wireless channel may be sharedby the Type 1 transceiver and/or the at least one Type 2 transceiver.The at least one Type 2 transceiver may transmit respective wirelesssignal contemporaneously using the common wireless system and/or thecommon wireless channel. The Type 1 transceiver may transmit a wirelesssignal to the at least one Type 2 transceiver using the common wirelesssystem and/or the common wireless channel.

A Type 1 device may temporarily function as a Type 2 device, and viceversa.

Each Type 1 device and Type 2 device may have at least onetransmitting/receiving antenna. Each CI may be associated with one ofthe transmitting antenna of the Type 1 device and one of the receivingantenna of the Type 2 device.

The at least one time series of CI may correspond to various antennapairs between the Type 1 device and the Type 2 device. The Type 1 devicemay have at least one antenna. The Type 2 device may also have at leastone antenna. Each time series of CI may be associated with an antenna ofthe Type 1 device and an antenna of the Type 2 device. Averaging orweighted averaging over antenna links may be performed. The averaging orweighted averaging may be over the at least one time series of CI. Theaveraging may optionally be performed on a subset of the at least onetime series of CI corresponding to a subset of the antenna pairs.

Time stamps of CI of a portion of a time series of CI may be irregularand may be corrected so that corrected timestamps of time-corrected CImay be uniformly spaced in time. In the case of multiple Type 1 devicesand/or multiple Type 2 devices, the corrected time stamp may be withrespect to the same or different clock.

An original timestamp associated with each of the CI may be determined.The original timestamp may not be uniformly spaced in time. Originaltimestamps of all CI of the particular portion of the particular timeseries of CI in the current sliding time window may be corrected so thatcorrected timestamps of time-corrected CI may be uniformly spaced intime.

The characteristics and/or spatial-temporal information may comprise: alocation, a position (e.g. initial position, new position), a positionon a map, a height, a horizontal location, a vertical location, adistance, a displacement, a speed, an acceleration, a rotational speed,a rotational acceleration, an angle of motion, direction of motion,rotation, a path, deformation, transformation, shrinking, expanding, agait, a gait cycle, a head motion, a repeated motion, a periodic motion,a pseudo-periodic motion, an impulsive motion, a sudden motion, afall-down motion, a transient motion, a behavior, a transient behavior,a period of motion, a frequency of motion, a time trend, a temporalprofile, a temporal characteristics, an occurrence, a change, a changein frequency, a change in timing, a change of gait cycle, a timing, astarting time, an ending time, a duration, a history of motion, a motiontype, a motion classification, a frequency, a frequency spectrum, afrequency characteristics, a presence, an absence, a proximity, anapproaching, a receding, an identity of the object, a composition of theobject, a head motion rate, a head motion direction, a mouth-relatedrate, an eye-related rate, a breathing rate, a heart rate, a hand motionrate, a hand motion direction, a leg motion, a body motion, a walkingrate, a hand motion rate, a positional characteristics, acharacteristics associated with movement of the object, a tool motion, amachine motion, a complex motion, and/or a combination of multiplemotions, an event and/or another information. The processor sharescomputational workload with the Type 1 heterogeneous wireless device andType 2 heterogeneous wireless device.

The Type 1 device and/or Type 2 device may be a local device. The localdevice may be: a smart phone, a smart device, TV, sound bar, set-topbox, access point, router, repeater, remote control, speaker, fan,refrigerator, microwave, oven, coffee machine, hot water pot, utensil,table, chair, light, lamp, door lock, camera, microphone, motion sensor,security device, fire hydrant, garage door, switch, power adapter,computer, dongle, computer peripheral, electronic pad, sofa, tile,accessory, smart home device, smart vehicle device, smart office device,smart building device, smart manufacturing device, smart watch, smartglasses, smart clock, smart television, smart oven, smart refrigerator,smart air-conditioner, smart chair, smart table, smart accessory, smartutility, smart appliance, smart machine, smart vehicle, aninternet-of-thing (IoT) device, an internet-enabled device, a computer,a portable computer, a tablet, a smart house, a smart office, a smartbuilding, a smart parking lot, a smart system, and/or another device.

Each Type 1 device may be associated with a respective identify (ID).Each Type 2 device may also be associated with a respective identify(ID). The ID may comprise: a numeral, a combination of text and numbers,a name, a password, an account, an account ID, a web link, a webaddress, index to some information, and/or another ID. The ID may beassigned. The ID may be assigned by hardware (e.g. hardwired, via adongle and/or other hardware), software and/or firmware. The ID may bestored (e.g. in a database, in memory, in a server, in the cloud, storedlocally, stored remotely, stored permanently, stored temporarily) andmay be retrieved. The ID may be associated with at least one record,account, user, household, address, phone number, social security number,customer number, another ID, time stamp, and/or collection of data. TheID and/or part of the ID of a Type 1 device may be made available to aType 2 device. The ID may be used for registration, initialization,communication, identification, verification, detection, recognition,authentication, access control, cloud access, networking, socialnetworking, logging, recording, cataloging, classification, tagging,association, pairing, transaction, electronic transaction, and/orintellectual property control, by the Type 1 device and/or the Type 2device.

The object may be a person, passenger, child, older person, baby,sleeping baby, baby in a vehicle, patient, worker, high-value worker,expert, specialist, waiter, customer in a mall, traveler inairport/train station/bus terminal/shipping terminals,staff/worker/customer service personnel in afactory/mall/supermarket/office/workplace, serviceman in sewage/airventilation system/lift well, lifts in lift wells, elevator, inmate,people to be tracked/monitored, animal, a plant, a living object, a pet,dog, cat, smart phone, phone accessory, computer, tablet, portablecomputer, dongle, computing accessory, networked devices, WiFi devices,IoT devices, smart watch, smart glasses, smart devices, speaker, keys,smart key, wallet, purse, handbag, backpack, goods, cargo, luggage,equipment, motor, machine, air conditioner, fan, air conditioningequipment, light fixture, moveable light, television, camera, audioand/or video equipment, stationary, surveillance equipment, parts,signage, tool, cart, ticket, parking ticket, toll ticket, airplaneticket, credit card, plastic card, access card, food packaging, utensil,table, chair, cleaning equipment/tool, vehicle, car, cars in parkingfacilities, merchandise in a warehouse/store/supermarket/distributioncenter, boat, bicycle, airplane, drone, remote control car/plane/boat,robot, manufacturing device, assembly line, material/unfinishedpart/robot/wagon/transports on a factory floor, object to be tracked inairport/shopping mart/supermarket, non-object, absence of an object,presence of an object, object with a form, object with a changing form,object with no form, mass of fluid, mass of liquid, mass of gas/smoke,fire, flame, electromagnetic (EM) source, EM medium, and/or anotherobject.

The object itself may be communicatively coupled with some network, suchas WiFi, MiFi, 3G/4G/LTE/5G, Bluetooth, BLE, WiMax, Zigbee, meshnetwork, adhoc network, and/or other network. The object itself may bebulky with AC power supply, but is moved during installation, cleaning,maintenance, renovation, etc. It may also be installed in a moveableplatform such as a lift, a pad, a movable, platform, an elevator, aconveyor belt, a robot, a drone, a forklift, a car, a boat, a vehicle,etc.

The object may have multiple parts, each part with different movement.For example, the object may be a person walking forward. While walking,his left hand and right hand may move in different direction, withdifferent instantaneous speed, acceleration, motion, etc.

The wireless transmitter (e.g. Type 1 device), the wireless receiver(e.g. Type 2 device), another wireless transmitter and/or anotherwireless receiver may move with the object and/or another object (e.g.in prior movement, current movement and/or future movement. They may becommunicatively coupled to one or more nearby device. They may transmittime series of CI and/or information associated with the time series ofCI to the nearby device, and/or each other. They may be with the nearbydevice.

The wireless transmitter and/or the wireless receiver may be part of asmall (e.g. coin-size, cigarette box size, or even smaller),light-weight portable device. The portable device may be wirelesslycoupled with a nearby device.

The nearby device may be smart phone, iPhone, Android phone, smartdevice, smart appliance, smart vehicle, smart gadget, smart TV, smartrefrigerator, smart speaker, smart watch, smart glasses, smart pad,iPad, computer, wearable computer, notebook computer, gateway. Thenearby device may be connected to a cloud server, a local server and/orother server via internet, wired internet connection and/or wirelessinternet connection. The nearby device may be portable.

The portable device, the nearby device, a local server and/or a cloudserver may share the computation and/or storage for a task (e.g. obtaintime series of CI, determine characteristics/spatial-temporalinformation of the object associated with the movement of the object,computation of time series of power information, determining/computingthe particular function, searching for local extremum, classification,identifying particular value of time offset, de-noising, processing,simplification, cleaning, wireless smart sensing task, extract CI fromwireless signal, switching, segmentation, estimate trajectory, processthe map, correction, corrective adjustment, adjustment, map-basedcorrection, detecting error, checking for boundary hitting,thresholding, etc.) and information (e.g. time series of CI).

The nearby device may/may not move with the object. The nearby devicemay be portable/not portable/moveable/non-moveable. The nearby devicemay use battery power, solar power, AC power and/or other power source.The nearby device may have replaceable/non-replaceable battery, and/orrechargeable/non-rechargeable battery. The nearby device may be similarto the object. The nearby device may have identical (and/or similar)hardware and/or software to the object. The nearby device may be a smartdevice, a network enabled device, a device with connection toWiFi/3G/4G/5G/6G/Zigbee/Bluetooth/adhoc network/other network, a smartspeaker, a smart watch, a smart clock, a smart appliance, a smartmachine, a smart equipment, a smart tool, a smart vehicle, aninternet-of-thing (IoT) device, an internet-enabled device, a computer,a portable computer, a tablet, and another device.

The nearby device and/or at least one processor associated with thewireless receiver, the wireless transmitter, the another wirelessreceiver, the another wireless transmitter and/or a cloud server (in thecloud) may determine the initial spatial-temporal information of theobject. Two or more of them may determine the initial spatial-temporalinfo jointly. Two or more of them may share intermediate information inthe determination of the initial spatial-temporal information (e.g.initial position).

In one example, the wireless transmitter (e.g. Type 1 device, or TrackerBot) may move with the object. The wireless transmitter may send thewireless signal to the wireless receiver (e.g. Type 2 device, or OriginRegister) or determining the initial spatial-temporal information (e.g.initial position) of the object. The wireless transmitter may also sendthe wireless signal and/or another wireless signal to another wirelessreceiver (e.g. another Type 2 device, or another Origin Register) forthe monitoring of the motion (spatial-temporal info) of the object. Thewireless receiver may also receive the wireless signal and/or anotherwireless signal from the wireless transmitter and/or the anotherwireless transmitter for monitoring the motion of the object. Thelocation of the wireless receiver and/or the another wireless receivermay be known.

In another example, the wireless receiver (e.g. Type 2 device, orTracker Bot) may move with the object. The wireless receiver may receivethe wireless signal transmitted from the wireless transmitter (e.g. Type1 device, or Origin Register) for determining the initialspatial-temporal info (e.g. initial position) of the object. Thewireless receiver may also receive the wireless signal and/or anotherwireless signal from another wireless transmitter (e.g. another Type 1device, or another Origin Register) for the monitoring of the currentmotion (e.g. spatial-temporal info) of the object. The wirelesstransmitter may also transmit the wireless signal and/or anotherwireless signal to the wireless receiver and/or the another wirelessreceiver (e.g. another Type 2 device, or another Tracker Bot) formonitoring the motion of the object. The location of the wirelesstransmitter and/or the another wireless transmitter may be known.

The venue may be a space such as a room, a house, an office, aworkplace, a hallway, a walkway, a lift, a lift well, an escalator, anelevator, a sewage system, air ventilations system, a staircase, agathering area, a duct, an air duct, a pipe, a tube, an enclosedstructure, a semi-enclosed structure, an enclosed area, an area with atleast one wall, a plant, a machine, an engine, a structure with wood, astructure with glass, a structure with metal, a structure with walls, astructure with doors, a structure with gaps, a structure with reflectionsurface, a structure with fluid, a building, a roof top, a store, afactory, an assembly line, a hotel room, a museum, a classroom, aschool, a university, a government building, a warehouse, a garage, amall, an airport, a train station, a bus terminal, a hub, atransportation hub, a shipping terminal, a government facility, a publicfacility, a school, a university, an entertainment facility, arecreational facility, a hospital, a seniors home, an elderly carefacility, a community center, a stadium, a playground, a park, a field,a sports facility, a swimming facility, a track and/or field, abasketball court, a tennis court, a soccer stadium, a baseball stadium,a gymnasium, a hall, a garage, a shopping mart, a mall, a supermarket, amanufacturing facility, a parking facility, a construction site, amining facility, a transportation facility, a highway, a road, a valley,a forest, a wood, a terrain, a landscape, a den, a patio, a land, apath, an amusement park, an urban area, a rural area, a suburban area, ametropolitan area, a garden, a square, a plaza, a music hall, a downtownfacility, an over-air facility, a semi-open facility, a closed area, atrain platform, a train station, a distribution center, a warehouse, astore, a distribution center, a storage facility, an undergroundfacility, a space (e.g. above ground, outer-space) facility, a floatingfacility, a cavern, a tunnel facility, an indoor facility, an open-airfacility, an outdoor facility with some walls/doors/reflective barriers,an open facility, a semi-open facility, a car, a truck, a bus, a van, acontainer, a ship/boat, a submersible, a train, a tram, an airplane, avehicle, a mobile home, a cave, a tunnel, a pipe, a channel, ametropolitan area, downtown area with relatively tall buildings, avalley, a well, a duct, a pathway, a gas line, an oil line, a waterpipe, a network of interconnectingpathways/alleys/roads/tubes/cavities/caves/pipe-like structure/airspace/fluid space, a human body, an animal body, a body cavity, anorgan, a bone, a teeth, a soft tissue, a hard tissue, a rigid tissue, anon-rigid tissue, a blood/body fluid vessel, windpipe, air duct, a den,etc. The venue may be an indoor space, an outdoor space, The venue mayinclude both the inside and outside of the space. For example, the venuemay include both the inside of a building and the outside of thebuilding. For example, the venue can be a building that has one floor ormultiple floors, and a portion of the building can be underground. Theshape of the building can be, e.g., round, square, rectangular,triangle, or irregular-shaped. These are merely examples. The disclosurecan be used to detect events in other types of venue or spaces.

The wireless transmitter (e.g. Type 1 device) and/or the wirelessreceiver (e.g. Type 2 device) may be embedded in a portable device (e.g.a module, or a device with the module) that may move with the object(e.g. in prior movement and/or current movement). The portable devicemay be communicatively coupled with the object using a wired connection(e.g. through USB, microUSB, Firewire, HDMI, serial port, parallel port,and other connectors) and/or a wireless connection (e.g. Bluetooth,Bluetooth Low Energy (BLE), WiFi, LTE, ZigBee, etc.). The portabledevice may be a lightweight device. The portable may be powered bybattery, rechargeable battery and/or AC power. The portable device maybe very small (e.g. at sub-millimeter scale and/or sub-centimeterscale), and/or small (e.g. coin-size, card-size, pocket-size, orlarger). The portable device may be large, sizable, and/or bulky (e.g.heavy machinery to be installed). The portable device may be a WiFihotspot, an access point, a mobile WiFi (MiFi), a dongle with USB/microUSB/Firew1ire/other connector, a smartphone, a portable computer, acomputer, a tablet, a smart device, an internet-of-thing (IoT) device, aWiFi-enabled device, an LTE-enabled device, a smart watch, a smartglass, a smart mirror, a smart antenna, a smart battery, a smart light,a smart pen, a smart ring, a smart door, a smart window, a smart clock,a small battery, a smart wallet, a smart belt, a smart handbag, a smartclothing/garment, a smart ornament, a smart packaging, a smartpaper/book/magazine/poster/printed matter/signage/display/lightedsystem/lighting system, a smart key/tool, a smartbracelet/chain/necklace/wearable/accessory, a smart pad/cushion, a smarttile/block/brick/building material/other material, a smart garbagecan/waste container, a smart food carriage/storage, a smart ball/racket,a smart chair/sofa/bed, a smart shoe/footwear/carpet/mat/shoe rack, asmart glove/hand wear/ring/hand ware, a smarthat/headwear/makeup/sticker/tattoo, a smart mirror, a smart toy, a smartpill, a smart utensil, a smart bottle/food container, a smart tool, asmart device, an IoT device, a WiFi enabled device, a network enableddevice, a 3G/4G/5G/6G enabled device, an embeddable device, animplantable device, air conditioner, refrigerator, heater, furnace,furniture, oven, cooking device, television/set-top box (STB)/DVDplayer/audio player/video player/remote control, hi-fi, audio device,speaker, lamp/light, wall, door, window, roof, rooftile/shingle/structure/atticstructure/device/feature/installation/fixtures, lawn mower/gardentools/yard tools/mechanics tools/garage tools/, garbage can/container,20-ft/40-ft container, storage container,factory/manufacturing/production device, repair tools, fluid container,machine, machinery to be installed, a vehicle, a cart, a wagon,warehouse vehicle, a car, a bicycle, a motorcycle, a boat, a vessel, anairplane, a basket/box/bag/bucket/container, a smartplate/cup/bowl/pot/mat/utensils/kitchen tools/kitchen devices/kitchenaccessories/cabinets/tables/chairs/tiles/lights/water pipes/taps/gasrange/oven/dishwashing machine/etc. The portable device may have abattery that may be replaceable, irreplaceable, rechargeable, and/ornon-rechargeable. The portable device may be wirelessly charged. Theportable device may be a smart payment card. The portable device may bea payment card used in parking lots, highways, entertainment parks, orother venues/facilities that need payment. The portable device may havean identity (ID) as described above.

An event may be monitored based on the time series of CI. The event maybe an object related event, such as fall-down of the object (e.g. anperson and/or a sick person), a rotation, a hesitation, a pause, animpact (e.g. a person hitting a sandbag, a door, a window, a bed, achair, a table, a desk, a cabinet, a box, another person, an animal, abird, a fly, a table, a chair, a ball, a bowling ball, a tennis ball, afootball, a soccer ball, a baseball, a basketball, a volley ball, etc.),a two-body action (e.g. a person letting go a balloon, catching a fish,molding a clay, writing a paper, a person typing on a computer, etc.), acar moving in a garage, a person carrying a smart phone and walkingaround an airport/mall/government building/office/etc, an autonomousmoveable object/machine moving around (e.g. vacuum cleaner, utilityvehicle, a car, drone, self-driving car, etc.).

The task or the wireless smart sensing task may comprise: objectdetection, presence detection, object recognition, object verification,tool detection, tool recognition, tool verification, machine detection,machine recognition, machine verification, human detection, humanrecognition, human verification, baby detection, baby recognition, babyverification, human breathing detection, motion detection, motionestimation, motion verification, periodic motion detection, periodicmotion estimation, periodic motion verification, stationary motiondetection, stationary motion estimation, stationary motion verification,cyclo-stationary motion detection, cyclo-stationary motion estimation,cyclo-stationary motion verification, transient motion detection,transient motion estimation, transient motion verification, trenddetection, trend estimation, trend verification, breathing detection,breathing estimation, breathing estimation, human biometrics detection,human biometrics estimation, human biometrics verification, environmentinformatics detection, environment informatics estimation, environmentinformatics verification, gait detection, gait estimation, gaitverification, gesture detection, gesture estimation, gestureverification, machine learning, supervised learning, unsupervisedlearning, semi-supervised learning, clustering, feature extraction,featuring training, principal component analysis, eigen-decomposition,frequency decomposition, time decomposition, time-frequencydecomposition, functional decomposition, other decomposition, training,discriminative training, supervised training, unsupervised training,semi-supervised training, neural network, sudden motion detection,fall-down detection, danger detection, life-threat detection, regularmotion detection, stationary motion detection, cyclo-stationary motiondetection, intrusion detection, suspicious motion detection, security,safety monitoring, navigation, guidance, map-based processing, map-basedcorrection, irregularity detection, locationing, tracking, multipleobject tracking, indoor tracking, indoor position, indoor navigation,power transfer, wireless power transfer, object counting, car trackingin parking garage, patient detection, patient monitoring, patientverification, wireless communication, data communication, signalbroadcasting, networking, coordination, administration, encryption,protection, cloud computing, other processing and/or other task. Thetask may be performed by the Type 1 device, the Type 2 device, anotherType 1 device, another Type 2 device, a nearby device, a local server,an edge server, a cloud server, and/or another device.

A first part of the task may comprise at least one of: preprocessing,signal conditioning, signal processing, post-processing, denoising,feature extraction, coding, encryption, transformation, mapping, motiondetection, motion estimation, motion change detection, motion patterndetection, motion pattern estimation, motion pattern recognition, vitalsign detection, vital sign estimation, vital sign recognition, periodicmotion detection, periodic motion estimation, breathing rate detection,breathing rate estimation, breathing pattern detection, breathingpattern estimation, breathing pattern recognition, heart beat detection,heart beat estimation, heart pattern detection, heart patternestimation, heart pattern recognition, gesture detection, gestureestimation, gesture recognition, speed detection, speed estimation,object locationing, object tracking, navigation, accelerationestimation, acceleration detection, fall-down detection, changedetection, intruder detection, baby detection, baby monitoring, patientmonitoring, object recognition, wireless power transfer, and/or wirelesscharging.

A second part of the task may comprise at least one of: a smart hometask, a smart office task, a smart building task, a smart factory task(e.g. manufacturing using a machine or an assembly line), a smartinternet-of-thing (IoT) task, a smart system task, a smart homeoperation, a smart office operation, a smart building operation, a smartmanufacturing operation (e.g. moving supplies/parts/raw material to amachine/an assembly line), an IoT operation, a smart system operation,turning on a light, turning off the light, controlling the light in atleast one of: a room, a region, and/or the venue, playing a sound clip,playing the sound clip in at least one of: the room, the region, and/orthe venue, playing the sound clip of at least one of: a welcome, agreeting, a farewell, a first message, and/or a second messageassociated with the first part of the task, turning on an appliance,turning off the appliance, controlling the appliance in at least one of:the room, the region, and/or the venue, turning on an electrical system,turning off the electrical system, controlling the electrical system inat least one of: the room, the region, and/or the venue, turning on asecurity system, turning off the security system, controlling thesecurity system in at least one of: the room, the region, and/or thevenue, turning on a mechanical system, turning off a mechanical system,controlling the mechanical system in at least one of: the room, theregion, and/or the venue, and/or controlling at least one of: an airconditioning system, a heating system, a ventilation system, a lightingsystem, a heating device, a stove, an entertainment system, a door, afence, a window, a garage, a computer system, a networked device, anetworked system, a home appliance, an office equipment, a lightingdevice, a robot (e.g. robotic arm), a smart vehicle, a smart machine, anassembly line, a smart device, an internet-of-thing (IoT) device, asmart home device, and/or a smart office device.

The task may include: detect a user returning home, detect a userleaving home, detect a user moving from one room to another,detect/control/lock/unlock/open/close/partially open awindow/door/garage door/blind/curtain/panel/solar panel/sun shade,detect a pet, detect/monitor a user doing something (e.g. sleeping onsofa, sleeping in bedroom, running on treadmill, cooking, sitting onsofa, watching TV, eating in kitchen, eating in dining room, goingupstairs/downstairs, going outside/coming back, in the rest room, etc.),monitor/detect location of a user/pet, do something automatically upondetection, do something for the user automatically upon detecting theuser, turn on/off/dim a light, turn on/off music/radio/homeentertainment system, turn on/off/adjust/control TV/HiFi/set-top-box(STB)/home entertainment system/smart speaker/smart device, turnon/off/adjust air conditioning system, turn on/off/adjust ventilationsystem, turn on/off/adjust heating system, adjust/control curtains/lightshades, turn on/off/wake a computer, turn on/off/pre-heat/control coffeemachine/hot water pot, turn on/off/control/preheat cooker/oven/microwaveoven/another cooking device, check/adjust temperature, check weatherforecast, check telephone message box, check mail, do a system check,control/adjust a system, check/control/arm/disarm security system/babymonitor, check/control refrigerator, give a report (e.g. through aspeaker such as Google home, Amazon Echo, on a display/screen, via awebpage/email/messaging system/notification system, etc.).

For example, when a user arrives home in his car, the task may be to,automatically, detect the user or his car approaching, open the garagedoor upon detection, turn on the driveway/garage light as the userapproaches the garage, turn on air conditioner/heater/fan, etc. As theuser enters the house, the task may be to, automatically, turn on theentrance light, turn off driveway/garage light, play a greeting messageto welcome the user, turn on the music, turn on the radio and tuning tothe user's favorite radio news channel, open the curtain/blind, monitorthe user's mood, adjust the lighting and sound environment according tothe user's mood or the current/imminent event (e.g. do romantic lightingand music because the user is scheduled to eat dinner with girlfriend in1 hour) on the user's daily calendar, warm the food in microwave thatthe user prepared in the morning, do a diagnostic check of all systemsin the house, check weather forecast for tomorrow's work, check news ofinterest to the user, check user's calendar and to-do list and playreminder, check telephone answer system/messaging system/email and givea verbal report using dialog system/speech synthesis, remind (e.g. usingaudible tool such as speakers/HiFi/speechsynthesis/sound/voice/music/song/sound field/background soundfield/dialog system, using visual tool such as TV/entertainmentsystem/computer/notebook/smartpad/display/light/color/brightness/patterns/symbols, using haptictool/virtual reality tool/gesture/tool, using a smartdevice/appliance/material/furniture/fixture, using web tool/server/cloudserver/fog server/edge server/home network/mesh network, using messagingtool/notification tool/communication tool/scheduling tool/email, usinguser interface/GUI, using scent/smell/fragrance/taste, using neuraltool/nervous system tool, using a combination, etc.) the user of hismother's birthday and to call her, prepare a report, and give the report(e.g. using a tool for reminding as discussed above). The task may turnon the air conditioner/heater/ventilation system in advance, or adjusttemperature setting of smart thermostat in advance, etc. As the usermoves from the entrance to the living room, the task may be to turn onthe living room light, open the living room curtain, open the window,turn off the entrance light behind the user, turn on the TV and set-topbox, set TV to the user's favorite channel, adjust an applianceaccording to the user's preference and conditions/states (e.g. adjustlighting and choose/play music to build a romantic atmosphere), etc.

Another example may be: When the user wakes up in the morning, the taskmay be to detect the user moving around in the bedroom, open theblind/curtain, open the window, turn off the alarm clock, adjust indoortemperature from night-time temperature profile to day-time temperatureprofile, turn on the bedroom light, turn on the restroom light as theuser approaches the restroom, check radio or streaming channel and playmorning news, turn on the coffee machine and preheat the water, turn offsecurity system, etc. When the user walks from bedroom to kitchen, thetask may be to turn on the kitchen and hallway lights, turn off thebedroom and restroom lights, move the music/message/reminder from thebedroom to the kitchen, turn on the kitchen TV, change TV to morningnews channel, lower the kitchen blind and open the kitchen window tobring in fresh air, unlock backdoor for the user to check the backyard,adjust temperature setting for the kitchen, etc.

Another example may be: When the user leaves home for work, the task maybe to detect the user leaving, play a farewell and/or have-a-good-daymessage, open/close garage door, turn on/off garage light and drivewaylight, turn off/dim lights to save energy (just in case the userforgets), close/lock all windows/doors (just in case the user forgets),turn off appliance (especially stove, oven, microwave oven), turn on/armthe home security system to guard the home against any intruder, adjustair conditioning/heating/ventilation systems to “away-from-home” profileto save energy, send alerts/reports/updates to the user's smart phone,etc.

A motion may comprise at least one of: a no-motion, a resting motion, anon-moving motion, a deterministic motion, a transient motion, afall-down motion, a repeating motion, a periodic motion, apseudo-periodic motion, a periodic motion associated with breathing, aperiodic motion associated with heartbeat, a periodic motion associatedwith a living object, a periodic motion associated with a machine, aperiodic motion associated with a man-made object, a periodic motionassociated with nature, a complex motion with a transient element and aperiodic element, a repetitive motion, a non-deterministic motion, aprobabilistic motion, a chaotic motion, a random motion, a complexmotion with a non-deterministic element and a deterministic element, astationary random motion, a pseudo-stationary random motion, acyclo-stationary random motion, a non-stationary random motion, astationary random motion with a periodic autocorrelation function (ACF),a random motion with a periodic ACF for a period of time, a randommotion that is pseudo-stationary for a period of time, a random motionof which an instantaneous ACF has a pseudo-periodic element for a periodof time, a machine motion, a mechanical motion, a vehicle motion, adrone motion, an air-related motion, a wind-related motion, aweather-related motion, a water-related motion, a fluid-related motion,an ground-related motion, a change in electro-magnetic characteristics,a sub-surface motion, a seismic motion, a plant motion, an animalmotion, a human motion, a normal motion, an abnormal motion, a dangerousmotion, a warning motion, a suspicious motion, a rain, a fire, a flood,a tsunami, an explosion, a collision, an imminent collision, a humanbody motion, a head motion, a facial motion, an eye motion, a mouthmotion, a tongue motion, a neck motion, a finger motion, a hand motion,an arm motion, a shoulder motion, a body motion, a chest motion, anabdominal motion, a hip motion, a leg motion, a foot motion, a bodyjoint motion, a knee motion, an elbow motion, an upper body motion, alower body motion, a skin motion, a below-skin motion, a subcutaneoustissue motion, a blood vessel motion, an intravenous motion, an organmotion, a heart motion, a lung motion, a stomach motion, an intestinemotion, a bowel motion, an eating motion, a breathing motion, a facialexpression, an eye expression, a mouth expression, a talking motion, asinging motion, an eating motion, a gesture, a hand gesture, an armgesture, a keystroke, a typing stroke, a user-interface gesture, aman-machine interaction, a gait, a dancing movement, a coordinatedmovement, and/or a coordinated body movement.

The heterogeneous IC of the Type 1 device and/or any Type 2 receiver maycomprise a low-noise amplifier (LNA), a power amplifier, atransmit-receive switch, a media access controller, a baseband radio, a2.4 GHz radio, a 3.65 GHz radio, a 4.9 GHz radio, a 5 GHz radio, a 5.9GHz radio, a below 6 GHz radio, a below 60 GHz radio and/or anotherradio.

The heterogeneous IC may comprise a processor, a memory communicativelycoupled with the processor, and a set of instructions stored in thememory to be executed by the processor. The IC may be an applicationspecific integrated circuit (ASIC), a field programmable gate array(FPGA), other programmable logic device, discrete logic, and/or acombination.

The heterogeneous IC may support a broadband network, a wirelessnetwork, a mobile network, a mesh network, a cellular network, awireless local area network (WLAN), a wide area network (WAN), and ametropolitan area network (MAN), a WLAN standard, WiFi, LTE, a 802.11standard, 802.11a, 802.11b, 802.11g, 802.11n, 802.11ac, 802.11ad,802.11af, 802.11ah, 802.11ax, 802.11ay, a mesh network standard, a802.15 standard, a 802.16 standard, a cellular network standard, 3G,3.5G, 4G, beyond 4G, 4.5G, 5G, 6G, 7G, 8G, 9G, Bluetooth, BluetoothLow-Energy (BLE), Zigbee, WiMax, and/or another wireless networkprotocol.

The processor may comprise a general purpose processor, a specialpurpose processor, a microprocessor, a microcontroller, an embeddedprocessor, a digital signal processor, a central processing unit (CPU),a graphical processing unit (GPU), a multi-processor, a multi-coreprocessor, and/or a processor with graphics capability, and/or acombination.

The memory may be volatile, non-volatile, random access memory (RAM),Read Only Memory (ROM), Electrically Programmable ROM (EPROM),Electrically Erasable Programmable ROM (EEPROM), hard disk, flashmemory, CD-ROM, DVD-ROM, a magnetic storage, an optical storage, anorganic storage, a storage system, a storage network, network storage,cloud storage, or other form of non-transitory storage medium known inthe art.

The set of instructions (machine executable code) corresponding to themethod steps may be embodied directly in hardware, in software, infirmware, or in combinations thereof.

The presentation may be a presentation in an audio-visual way, agraphical way (e.g. using GUI), a textual way, a symbolic way or amechanical way.

Computational workload associated with the method is shared among theprocessor, the Type 1 heterogeneous wireless device, the Type 2heterogeneous wireless device, a local server, a cloud server, andanother processor.

An operation, a pre-processing, a processing and/or a postprocessing maybe applied to data (e.g. time series of CI, autocorrelation). Anoperation may be preprocessing, processing and/or postprocessing. Thepreprocessing, processing and/or postprocessing may be an operation. Anoperation may comprise preprocessing, processing, post-processing,computing a function of the operands, filtering, linear filtering,nonlinear filtering, folding, grouping, energy computation, lowpassfiltering, bandpass filtering, highpass filtering, median filtering,rank filtering, quartile filtering, percentile filtering, modefiltering, finite impulse response (FIR) filtering, infinite impulseresponse (IIR) filtering, moving average (MA) filtering, autoregressive(AR) filtering, autoregressive moving averaging (ARMA) filtering,selective filtering, adaptive filtering, interpolation, decimation,subsampling, upsampling, resampling, time correction, time basecorrection, phase correction, magnitude correction, phase cleaning,magnitude cleaning, matched filtering, enhancement, restoration,denoising, smoothing, signal conditioning, enhancement, restoration,spectral analysis, linear transform, nonlinear transform, frequencytransform, inverse frequency transform, Fourier transform, wavelettransform, Laplace transform, Hilbert transform, Hadamard transform,trigonometric transform, sine transform, cosine transform, power-of-2transform, sparse transform, graph-based transform, graph signalprocessing, fast transform, a transform combined with zero padding,cyclic padding, padding, zero padding, feature extraction,decomposition, projection, orthogonal projection, non-orthogonalprojection, over-complete projection, eigen-decomposition, singularvalue decomposition (SVD), principle component analysis (PCA),independent component analysis (ICA), grouping, sorting, thresholding,soft thresholding, hard thresholding, clipping, soft clipping, firstderivative, second order derivative, high order derivative, convolution,multiplication, division, addition, subtraction, integration,maximization, minimization, local maximization, local minimization,optimization of a cost function, neural network, recognition, labeling,training, clustering, machine learning, supervised learning,unsupervised learning, semi-supervised learning, comparison with anothertime series of CI, similarity score computation, quantization, vectorquantization, matching pursuit, compression, encryption, coding,storing, transmitting, normalization, temporal normalization, frequencydomain normalization, classification, clustering, labeling, tagging,learning, detection, estimation, learning network, mapping, remapping,expansion, storing, retrieving, transmitting, receiving, representing,merging, combining, splitting, tracking, monitoring, matched filtering,Kalman filtering, particle filter, intrapolation, extrapolation,importance sampling, Monte Carlo sampling, compressive sensing,representing, merging, combining, splitting, scrambling, errorprotection, forward error correction, doing nothing, time varyingprocessing, conditioning averaging, weighted averaging, arithmetic mean,geometric mean, averaging over selected frequency, averaging overantenna links, a logical operation, permutation, combination, sorting,AND, OR, XOR, union, intersection, vector addition, vector subtraction,vector multiplication, vector division, inverse, a norm, a distance,and/or another operation. The operation may be the preprocessing,processing, and/or post-processing. Operations may be applied jointly onmultiple time series or functions.

The function (e.g. function of the operands) may comprise: a scalarfunction, a vector function, a discrete function, a continuous function,a polynomial function, a magnitude, a phase, an exponential function, alogarithmic function, a trigonometric function, a transcendentalfunction, a logical function, a linear function, an algebraic function,a nonlinear function, a piecewise linear function, a real function, acomplex function, a vector-valued function, an inverse function, aderivative of a function, an integration of a function, a circularfunction, a function of another function, a one-to-one function, aone-to-many function, a many-to-one function, a many-to-many function, azero crossing, absolute function, indicator function, a mean, a mode, amedian, a range, a statistics, a variance, a trimmed mean, a percentile,a square, a cube, a root, a power, a sine, a cosine, a tangent, acotangent, a secant, a cosecant, an elliptical function, a parabolicfunction, a hyperbolic function, a game function, a zeta function, anabsolute value, a thresholding, a quantization, a piecewise constantfunction, a composite function, a function of function, an input timefunction processed with an operation (e.g. filtering), a probabilisticfunction, a stochastic function, a random function, an ergodic function,a stationary function, a deterministic function, a transformation, afrequency transform, an inverse frequency transform, a discrete timetransform, Laplace transform, Hilbert transform, sine transform, cosinetransform, triangular transform, wavelet transform, integer transform,power-of-2 transform, sparse transform, projection, decomposition,principle component analysis (PCA), independent component analysis(ICA), neural network, feature extraction, a moving function, a functionof a moving window of neighboring items of a time series, a filteringfunction, a convolution, a mean function, a variance function, astatistical function, short-time transform, discrete transform, discreteFourier transform, discrete cosine transform, discrete sine transform,Hadamard transform, eigen-decomposition, eigenvalue, singular valuedecomposition (SVD), singular value, orthogonal decomposition, matchingpursuit, sparse transform, sparse approximation, any decomposition,graph-based processing, graph-based transform, graph signal processing,classification, labeling, learning, machine learning, detection,estimation, feature extraction, learning network, feature extraction,denoising, signal enhancement, coding, encryption, mapping, remapping,vector quantization, lowpass filtering, highpass filtering, bandpassfiltering, matched filtering, Kalman filtering, preprocessing,postprocessing, particle filter, FIR filtering, IIR filtering,autoregressive (AR) filtering, adaptive filtering, first orderderivative, high order derivative, integration, zero crossing,smoothing, median filtering, mode filtering, sampling, random sampling,resampling function, downsampling, upsampling, interpolation,extrapolation, importance sampling, Monte Carlo sampling, compressivesensing, statistics, short term statistics, long term statistics, mean,variance, autocorrelation function, cross correlation, moment generatingfunction, time averaging, etc.

Machine learn, training, discriminative training, deep learning, neuralnetwork, continuous time processing, distributed computing, distributedstorage, acceleration usingGPU/DSP/coprocessor/multicore/multiprocessing may be applied to a step(or each step) of this disclosure.

A frequency transform may include Fourier transform, Laplace transform,Hadamard transform, Hilbert transform, sine transform, cosine transform,triangular transform, wavelet transform, integer transform, power-of-2transform, combined zero padding and transform, Fourier transform withzero padding, and/or another transform. Fast versions and/orapproximated versions of the transform may be performed. The transformmay be performed using floating point, and/or fixed point arithmetic.

An inverse frequency transform may include inverse Fourier transform,inverse Laplace transform, inverse Hadamard transform, inverse Hilberttransform, inverse sine transform, inverse cosine transform, inversetriangular transform, inverse wavelet transform, inverse integertransform, inverse power-of-2 transform, combined zero padding andtransform, inverse Fourier transform with zero padding, and/or anothertransform. Fast versions and/or approximated versions of the transformmay be performed. The transform may be performed using floating point,and/or fixed point arithmetic.

Sliding time window may have time varying window width. It may besmaller at the beginning to enable fast acquisition and may increaseover time to a steady-state size. The steady-state size may be relatedto the frequency, repeated motion, transient motion, and/orspatial-temporal information to be monitored. Even in steady state, thewindow size may be adaptively changed based on battery life, powerconsumption, available computing power, a change in amount of targets,the nature of motion to be monitored, etc.

The time shift between two sliding time windows at adjacent timeinstance may be constant/variable/locally adaptive over time. Whenshorter time shift is used, the update of any monitoring may be morefrequent which may be used for fast changing situations, object motions,and/or objects. Longer time shift may be used for slower situations,object motions, and/or objects.

The window width/size and/or time shift may be changed upon a userrequest/choice. The time shift may be changed automatically (e.g. ascontrolled by processor/computer/server/cloud server) and/or adaptively.

At least one characteristics of a function (e.g. auto-correlationfunction, auto-covariance function, cross-correlation function,cross-covariance function, power spectral density, a time function, afrequency domain function, a frequency transform) may be determined(e.g. by an object tracking server, the processor, the Type 1heterogeneous device, the Type 2 heterogeneous device, and/or anotherdevice). The at least one characteristics of the function may include: alocal maximum, a local minimum, a local extremum, a local extremum withpositive time offset, a first local extremum with positive time offset,an n{circumflex over ( )}th local extremum with positive time offset, alocal extremum with negative time offset, a first local extremum withnegative time offset, an n{circumflex over ( )}th local extremum withnegative time offset, a constrained (with argument within a constraint)maximum, minimum, constrained maximum, constrained minimum, aconstrained extremum, a slope, a derivative, a higher order derivative,a maximum slope, a minimum slope, a local maximum slope, a local maximumslope with positive time offset, a local minimum slope, a constrainedmaximum slope, a constrained minimum slope, a maximum higher orderderivative, a minimum higher order derivative, a constrained higherorder derivative, a zero-crossing, a zero crossing with positive timeoffset, an n{circumflex over ( )}th zero crossing with positive timeoffset, a zero crossing with negative time offset, an n{circumflex over( )}th zero crossing with negative time offset, a constrainedzero-crossing, a zero-crossing of slope, a zero-crossing of higher orderderivative, and/or another characteristics. At least one argument of thefunction associated with the at least one characteristics of thefunction may be identified. Some quantity (e.g. a spatial-temporalinformation of the object) may be determined based on the at least oneargument of the function.

A characteristics of a motion of an object in the venue may comprise atleast one of: an instantaneous characteristics, a short-termcharacteristics, repetitive characteristics, a recurringcharacteristics, a history, an incremental characteristics, a changingcharacteristics, a deviational characteristics, a phase, a magnitude, atime characteristics, a frequency characteristics, a time-frequencycharacteristics, a decomposition characteristics, an orthogonaldecomposition characteristics, a non-orthogonal decompositioncharacteristics, a deterministic characteristics, a probabilisticcharacteristics, a stochastic characteristics, an autocorrelationfunction (ACF), a mean, a variance, a statistics, a duration, a timing,a trend, a periodic characteristics, a long-term characteristics, ahistorical characteristics, an average characteristics, a currentcharacteristics, a past characteristics, a future characteristics, apredicted characteristics, a location, a distance, a height, a speed, adirection, a velocity, an acceleration, a change of the acceleration, anangle, an angular speed, an angular velocity, an angular acceleration ofthe object, a change of the angular acceleration, an orientation of theobject, an angular of a rotation, a deformation of the object, a shapeof the object, a change of shape of the object, a change of size of theobject, a change of structure of the object, and/or a change ofcharacteristics of the object.

At least one local maximum and at least one local minimum of thefunction may be identified. At least one localsignal-to-noise-ratio-like (SNR-like) parameter may be computed for eachpair of adjacent local maximum and local minimum. The SNR-like parametermay be a function (e.g. linear, log, exponential function, a monotonicfunction) of a fraction of a quantity (e.g. power, magnitude, etc.) ofthe local maximum over the same quantity of the local minimum. It mayalso be the function of a difference between the quantity of the localmaximum and the same quantity of the local minimum.

Significant local peaks may be identified or selected. Each significantlocal peak may be a local maximum with SNR-like parameter greater than athreshold T1 and/or a local maximum with amplitude greater than athreshold T2.

The at least one local minimum and the at least one local minimum in thefrequency domain may be identified/computed using a persistence-basedapproach.

A set of selected significant local peaks may be selected from the setof identified significant local peaks based on a selection criterion.The characteristics/spatial-temporal information of the object may becomputed based on the set of selected significant local peaks andfrequency values associated with the set of selected significant localpeaks.

In one example, the selection criterion may always correspond to selectthe strongest peaks in a range. While the strongest peaks may beselected, the unselected peaks may still be significant (rather strong).

Unselected significant peaks may be stored and/or monitored as“reserved” peaks for use in future selection in future sliding timewindows. As an example, there may be a particular peak (at a particularfrequency) appearing consistently over time. Initially, it may besignificant but not selected (as other peaks may be stronger). But inlater time, the peak may become stronger and more dominant and may beselected. When it became “selected”, it may be back-traced in time andmade “selected” in the earlier time when it was significant but notselected. In such case, the back-traced peak may replace a previouslyselected peak in an early time. The replaced peak may be the relativelyweakest, or a peak that appear in isolation in time (i.e. appearing onlybriefly in time).

In another example, the selection criterion may not correspond to selectthe strongest peaks in the range. Instead, it may consider not only the“strength” of the peak, but the “trace” of the peak—peaks that may havehappened in the past, especially those peaks that have been identifiedfor a long time.

For example, if a finite state machine (FSM) is used, it may select thepeak(s) based on the state of the FSM. Decision thresholds may becomputed adaptively based on the state of the FSM.

A similarity score may be computed (e.g. by a server, the processor, theType 1 device, the Type 2 device, a local server, a cloud server, and/oranother device) based on a pair of temporally adjacent CI of a timeseries of CI. The pair may come from the same sliding window or twodifferent sliding windows. The similarity score may also be based on apair of, temporally adjacent or not so adjacent, CI from two differenttime series of CI. The similarity score may be or may include: a timereversal resonating strength (TRRS), a correlation, a cross-correlation,an auto-correlation, a covariance, a cross-covariance, anauto-covariance, an inner product of two vectors, a distance score, adiscrimination score, a metric, a neural network output, a deep learningnetwork output, and/or another score. The characteristics and/orspatial-temporal information may be determined/computed based on thesimilarity score.

Any threshold may be pre-determined, adaptively determined and/ordetermined by a finite state machine. The adaptive determination may bebased on time, space, location, antenna, path, link, state, batterylife, remaining battery life, available power, available computationalresources, available network bandwidth, etc.

A threshold to be applied to a test statistics to differentiate twoevents (or two conditions, or two situations, or two states), A and B,may be determined. Data (e.g. CI, channel state information (CSI)) maybe collected under A and/or under B in a training situation. The teststatistics may be computed based on the data. Distributions of the teststatistics under A may be compared with distributions of the teststatistics under B, and the threshold may be chosen according to somecriteria. The criteria may comprise: maximum likelihood (ML), maximumaposterior probability (MAP), discriminative training, minimum type 1error for a given type 2 error, minimum type 2 error for a given type 1error, and/or other criteria. The threshold may be adjusted to achievedifferent sensitivity to the A, B and/or anotherevent/condition/situation/state. The threshold adjustment may beautomatic, semi-automatic and/or manual. The threshold adjustment may beapplied once, sometimes, often, periodically, occasionally,sporadically, and/or on demand. The threshold adjustment may beadaptive. The threshold adjustment may depend on the object, an objectmovement/location/direction/action, an objectcharacteristics/spatial-temporalinformation/size/property/trait/habit/behavior, the venue, afeature/fixture/furniture/barrier/material/machine/livingthing/thing/object/boundary/surface/medium that is in/at/of the venue, amap, a constraint of the map, the event/state/situation/condition, atime, a timing, a duration, a current state, a past history, a user,and/or a personal preference, etc.

A stopping criterion of an iterative algorithm may be that change of acurrent parameter (e.g. offset value) in the updating in an iteration isless than a threshold. The threshold may be 0.5, 1, 1.5, 2, or anothernumber. The threshold may be adaptive. It may change as the iterationprogresses. For the offset value, the adaptive threshold may bedetermined based on the task, particular value of the first time, thecurrent time offset value, the regression window, the regressionanalysis, the regression function, the regression error, the convexityof the regression function, and/or an iteration number.

The local extremum may be determined as the corresponding extremum ofthe regression function in the regression window. The local extremum maybe determined based on a set of time offset values in the regressionwindow and a set of associated regression function values. Each of theset of associated regression function values associated with the set oftime offset values may be within a range from the corresponding extremumof the regression function in the regression window.

The searching for a local extremum may comprise a robust search, aminimization, a maximization, an optimization, a statisticaloptimization, a dual optimization, a constraint optimization, a convexoptimization, a global optimization, a local optimization an energyminimization, a linear regression, a quadratic regression, a higherorder regression, a linear programming, a nonlinear programming, astochastic programming, a combinatorial optimization, a constraintprogramming, a constraint satisfaction, a calculus of variations, anoptimal control, a dynamic programming, a mathematical programming, amulti-objective optimization, a multi-modal optimization, a disjunctiveprogramming, a space mapping, an infinite-dimensional optimization, aheuristics, a metaheuristics, a convex programming, a semidefiniteprogramming, a conic programming, a cone programming, an integerprogramming, a quadratic programming, a fractional programming, anumerical analysis, a simplex algorithm, an iterative method, a gradientdescent, a subgradient method, a coordinate descent, a conjugategradient method, a Newton's algorithm, a sequential quadraticprogramming, an interior point method, an ellipsoid method, a reducedgradient method, a quasi-Newton method, a simultaneous perturbationstochastic approximation, an interpolation method, a pattern searchmethod, a line search, a non-differentiable optimization, a geneticalgorithm, an evolutionary algorithm, a dynamic relaxation, a hillclimbing, a particle swarm optimization, a gravitation search algorithm,a simulated annealing, a memetic algorithm, a differential evolution, adynamic relaxation, a stochastic tunneling, a Tabu search, a reactivesearch optimization, a curve fitting, a least square, a simulation basedoptimization, a variational calculus, and/or a variant. The search for alocal extremum may be associated with an objective function, a lossfunction, a cost function, a utility function, a fitness function, anenergy function, and/or an energy function.

Regression may be performed using a regression function to fit sampleddata (e.g. CI, a feature of CI, a component of CI) or another function(e.g. autocorrelation function) in a regression window. In at least oneiteration, a length of the regression window and/or a location of theregression window may change. The regression function may be a linearfunction, a quadratic function, a cubic function, a polynomial function,and/or another function.

The regression analysis may minimize an absolute error, a square error,a higher order error (e.g. third order, fourth order, etc.), a robusterror (e.g. square error for smaller error magnitude and absolute errorfor larger error magnitude, or a first kind of error for smaller errormagnitude and a second kind of error for larger error magnitude),another error, a weighted sum of absolute error (e.g. for a wirelesstransmitter with multiple antennas and a wireless receiver with multipleantennas, each pair of transmitter antenna and receiver antenna form alink. Error associated with different links may have different weights.One possibility is that some links and/or some components with largernoise may have smaller or bigger weight.), a weighted sum of squareerror, a weighted sum of higher order error, a weighted sum of robusterror, a weighted sum of the another error, an absolute cost, a squarecost, a higher order cost, a robust cost, another cost, a weighted sumof absolute cost, a weighted sum of square cost, a weighted sum ofhigher order cost, a weighted sum of robust cost, and/or a weighted sumof another cost.

The regression error determined may be an absolute error, a squareerror, a higher order error, a robust error, yet another error, aweighted sum of absolute error, a weighted sum of square error, aweighted sum of higher order error, a weighted sum of robust error,and/or a weighted sum of the yet another error.

The time offset associated with maximum regression error (or minimumregression error) of the regression function with respect to theparticular function in the regression window may become the updatedcurrent time offset in the iteration.

A local extremum may be searched based on a quantity comprising adifference of two different errors (e.g. a difference between absoluteerror and square error). Each of the two different errors may comprisean absolute error, a square error, a higher order error, a robust error,another error, a weighted sum of absolute error, a weighted sum ofsquare error, a weighted sum of higher order error, a weighted sum ofrobust error, and/or a weighted sum of the another error.

The quantity may be compared with an F-distribution, a centralF-distribution, another statistical distribution, a threshold, athreshold associated with a probability, a threshold associated with aprobability of finding a false peak, a threshold associated with theF-distribution, a threshold associated the central F-distribution,and/or a threshold associated with the another statistical distribution.

The regression window may be determined based on at least one of: themovement of the object, a quantity associated with the object, the atleast one characteristics and/or spatial-temporal information of theobject associated with the movement of the object, an estimated locationof the local extremum, a noise characteristics, an estimated noisecharacteristics, an F-distribution, a central F-distribution, anotherstatistical distribution, a threshold, a preset threshold, a thresholdassociated with a probability, a threshold associated with a desiredprobability, a threshold associated with a probability of finding afalse peak, a threshold associated with the F-distribution, a thresholdassociated the central F-distribution, a threshold associated with theanother statistical distribution, a condition that a quantity at thewindow center is largest within the regression window, a condition thatthe quantity at the window center is largest within the regressionwindow, a condition that there is only one of the local extremum of theparticular function for the particular value of the first time in theregression window, another regression window, and/or another condition.

The width of the regression window may be determined based on theparticular local extremum to be searched. The local extremum maycomprise first local maximum, second local maximum, higher order localmaximum, first local maximum with positive time offset value, secondlocal maximum with positive time offset value, higher local maximum withpositive time offset value, first local maximum with negative timeoffset value, second local maximum with negative time offset value,higher local maximum with negative time offset value, first localminimum, second local minimum, higher local minimum, first local minimumwith positive time offset value, second local minimum with positive timeoffset value, higher local minimum with positive time offset value,first local minimum with negative time offset value, second localminimum with negative time offset value, higher local minimum withnegative time offset value, first local extremum, second local extremum,higher local extremum, first local extremum with positive time offsetvalue, second local extremum with positive time offset value, higherlocal extremum with positive time offset value, first local extremumwith negative time offset value, second local extremum with negativetime offset value, and/or higher local extremum with negative timeoffset value.

A current parameter (e.g. time offset value) may be initialized based ona target value, a target profile, a trend, a past trend, a currenttrend, a target speed, a speed profile, a target speed profile, a pastspeed trend, the movement of the object, at least one characteristicsand/or spatial-temporal information of the object associated with themovement of object, a positional quantity of the object, an initialspeed of the object associated with the movement of the object, apredefined value, an initial width of the regression window, a timeduration, a value based on a carrier frequency of the wireless signal, abandwidth of the wireless signal, an amount of antennas associated withthe wireless multipath channel, a noise characteristics, and/or anadaptive value. The current time offset may be at the center, on theleft side, on the right side, and/or at another fixed relative location,of the regression window.

FIG. 1 illustrates an exemplary network environment 100 for vital signdetection and monitoring in a venue, according to one embodiment of thepresent teaching. As shown in FIG. 1, the exemplary network environment100 includes a transmitter 110, an antenna 112, a wireless channel 130,an antenna 122, and a receiver 120. The antenna 112 is electricallycoupled to the transmitter 110; the antenna 122 is electrically coupledto the receiver 120.

In one embodiment, the transmitter 110 is located at a first position ina venue; while the receiver 120 is located at a second position in thevenue. The transmitter 110 is configured for transmitting a wirelesssignal through the wireless channel 130. The wireless channel 130 inthis example is a wireless multipath channel that is impacted by apseudo-periodic motion of an object in the venue. According to variousembodiments, the object may be a human (e.g. a baby 142, or a patient146) or a pet (e.g. a puppy 144). The receiver 120 in this examplereceives the wireless signal through the wireless multipath channel 130and obtains at least one time series of channel information (CI) of thewireless multipath channel based on the wireless signal. Because themotion of the object impacts the wireless multipath channel throughwhich the wireless signal is transmitted, the channel information 125extracted from the wireless signal includes information related to theobject motion.

In various embodiments, the transmitter 110 may be part of a Bot or aType 1 device placed in a venue, while the receiver 120 may be part ofan Origin or a Type 2 device placed in the venue. In variousembodiments, the Bot and/or the Origin may include (not shown) multipletransmitters, multiple receivers, and/or multiple transceivers. In oneembodiment, the antenna 112 and/or the antenna 122 is replaced with amulti-antenna array that can form a plurality of beams each of whichpoints in a distinct direction. The transmitter 110 can be configured towirelessly transmit signals having different types or functions.Similarly, the receiver 120 is configured to receive wireless signalshaving different types or functions. In one embodiment, the transmitter110 has at least one antenna; and the receiver 120 has at least oneantenna. Each of the at least one time series of CI is associated withone of the at least one antenna of the transmitter 110 and one of the atleast one antenna of the receiver 120.

FIG. 2 illustrates an exemplary block diagram of a system 200 for vitalsign detection and monitoring, according to one embodiment of thepresent teaching. As shown in FIG. 2, the exemplary vital signmonitoring system 200 comprises a vital sign estimator 210 and a systemstate controller 220. The vital sign estimator 210 receives an input ofa time series of channel information (TSCI) 205, which may be thechannel information 125 generated by the receiver 120. According tovarious embodiments, the system can detect and monitor various periodicand pseudo-periodic motions, whether they represent vital signs or not.

In one embodiment, the system 200 may comprise the transmitter 110, thereceiver 120 and their respective antennas as well. The vital signestimator may be coupled to at least one of: the transmitter 110, thereceiver 120, an additional transmitter, an additional receiver, a cloudserver, a fog server, a local server, and an edge server.

In one embodiment, the vital sign estimator 210 is configured fordetermining that at least one portion of the TSCI 205 in a currentsliding time window is associated with a pseudo-periodic motion of theobject (e.g. breathing of a person) in the venue, and computing acurrent characteristics (e.g. a repetition rate, a breathing rate)related to the pseudo-periodic motion of the object in the currentsliding time window based on at least one of: the at least one portionof the TSCI 205 in the current sliding time window, at least one portionof the at least one TSCI 205 in a past sliding time window, and a pastcharacteristics related to the pseudo-periodic motion of the object inthe past sliding time window. Here, a “portion” of a time series of CImay be those CI in the corresponding time window such as current timewindow, or past time window. The computation may be based on acomparison between a current sliding time window of the at least oneportion of the at least one TSCI 205 with a past sliding time window ofthe at least one portion of the at least one TSCI 205, and maybe basedon the past characteristics. A repetition rate (e.g. breathing rate) canbe represented by the repetition frequency (e.g. breathing frequency),e.g. 20 bpm. It can also be a period corresponding to the breathingfrequency, e.g. 3 seconds for 20 bpm (60/20=3).

According to various embodiments, at least one of the currentcharacteristics and the past characteristics comprises informationrelated to at least one of: a frequency of pseudo-periodic motion, afrequency characteristics, a frequency spectrum, a time period of pseudoperiodic motion, a temporal characteristics, a temporal profile, atiming of pseudo-periodic motion, a starting time, an ending time, aduration, a history of motion, a motion type, a motion classification, alocation of the object, a speed, a displacement, an acceleration, arotational speed, a rotational characteristics, a gait cycle of theobject, a transient behavior of the object, a transient motion, a changein pseudo-periodic motion, a change in frequency of pseudo-periodicmotion, a change in gait cycle, an event associated with pseudo-periodicmotion, an event associated with transient motion, a sudden-motionevent, and a fall-down event.

In one embodiment, the vital sign estimator 210 is further configuredfor making the current characteristics related to the pseudo-periodicmotion of the object available in real time; and moving the currentsliding time window by a shift-size as time progresses.

In one embodiment, the system also includes an additional transmitterlocated at a third position in the venue and configured for transmittingan additional wireless signal through the wireless multipath channel 130impacted by the pseudo-periodic motion of the object in the venue; andan additional receiver located at a fourth position in the venue andconfigured for: receiving the additional wireless signal through thewireless multipath channel, and obtaining an additional time series ofchannel information (CI) of the wireless multipath channel 130 based onthe additional wireless signal. In this embodiment, the vital signestimator 210 is further configured for: determining that at least oneportion of the additional TSCI in the current sliding time window isassociated with the pseudo-periodic motion of the object in the venue,and computing the current characteristics related to the pseudo-periodicmotion of the object in the current sliding time window based on atleast one of: the at least one portion of the additional TSCI in thecurrent sliding time window, at least one portion of the additional TSCIin an additional past sliding time window, and a past characteristicsrelated to the pseudo-periodic motion of the object in the additionalpast sliding time window. To implement this embodiment, a system mayinclude more than one pair of Bot and Origin, each pair giving rise toindependent CI. Repetition rate (e.g. breathing rate) may be computedindividually based on the individual pair or jointly based on bothpairs. Results of two different Bot-Origin pairs may be combined. Eachpair may use different method to obtain the repetition rate (e.g.breathing rate). Thresholds may be different. The two pairs may have acommon Bot, or a common Origin.

In another embodiment, two or more pseudo-periodic object motions aremonitored at the same time. For example, the same currentcharacteristics (e.g. repetition rate, breathing rate) of the two peopleare computed based on the same current sliding window of CI. In thiscase, the vital sign estimator 210 is further configured for:determining that at least one portion of the at least one TSCI in thecurrent sliding time window is associated with an additionalpseudo-periodic motion of an additional object in the venue, wherein thewireless multipath channel is further impacted in the current slidingtime window by the additional pseudo-periodic motion of the additionalobject; and computing a current characteristics related to theadditional pseudo-periodic motion of the additional object in thecurrent sliding time window based on at least one of: the at least oneportion of the at least one TSCI in the current sliding time window, theat least one portion of the at least one TSCI in the past sliding timewindow, and a past characteristics related to the additionalpseudo-periodic motion of the additional object in the past sliding timewindow. In one example, while the pseudo-periodic motion representsbreathing, the additional pseudo-periodic motion represents heartbeat.

In another embodiment, the number of objects having pseudo-periodicmotions is estimated. In this case, the vital sign estimator 210 isfurther configured for: determining that the wireless multipath channelis impacted in the current sliding time window by pseudo-periodicmotions of a plurality of objects; computing current characteristicsrelated to pseudo-periodic motions of the plurality of objects in thecurrent sliding time window based on at least one of: the at least oneportion of the at least one TSCI in the current sliding time window, theat least one portion of the at least one TSCI in the past sliding timewindow, and a past characteristics related to the pseudo-periodicmotions of the plurality of objects in the past sliding time window; andestimating a quantity of the plurality of objects based on the currentcharacteristics.

The system state controller 220 in this example may comprise a finitestate machine (FSM) configured for tuning parameters (e.g. peakdetection thresholds under different states) of the system 200, for thesystem to output an accurate vital sign estimation 225, which mayinclude an estimate of the vital sign characteristics (e.g. thebreathing rate) and/or the quantity of objects (e.g. persons that arebreathing). In one embodiment, the system state controller 220 isconfigured for computing adaptively at least one of: decisionthresholds, threshold T1, threshold T2, lower bound associated withfrequency of the selected significant local peaks, upper boundassociated with frequency of the selected significant local peaks,search range, and a parameter associated with a selection of theselected significant local peaks, based on a finite state machine (FSM).The FSM may comprise at least one of: an Initiation (INIT) state, aVerification state, a PeakFound state, and a Motion state.

In one embodiment, the system state controller 220 is further configuredfor: entering the INIT state of the FSM in at least one predeterminedway; computing the thresholds adaptively in a first way in the INITstate; detecting an event based on the thresholds; transitioning fromthe INIT state to a different state of the FSM based on at least onetransition criterion; and computing the thresholds adaptively in asecond way in the different state. In another embodiment, the systemstate controller 220 is further configured for: entering the INIT stateof the FSM; computing the thresholds adaptively in a first way;detecting an event based on the thresholds and at least one of: the setof selected significant local peaks and related characteristics;detecting excessive background interfering motion based on thethresholds and the remaining spectral energy; staying in the INIT statewhen the event is concluded to be “not detected” and the excessivebackground interfering motion is concluded to be “not detected;”transitioning from the INIT state to the Verification state when theevent is concluded to be “detected” preliminarily and the detected eventneeds to be verified; and transitioning from the INIT state to theMotion state when the excessive background interfering motion isconcluded to be “detected.”

In another embodiment, the system state controller 220 is furtherconfigured for: in the Verification state: computing the thresholdsadaptively in a second way; accumulating and computing at least onestatistics based on sets of selected significant local peaks and relatedcharacteristics in at least one adjacent sliding time window forverification of the detected event that is detected preliminarily;staying in the Verification state while the at least one statistics isbeing accumulated until sufficient statistics is collected for theverification; verifying the preliminarily detected event based on thethresholds and the at least one statistics; transitioning from theVerification state to the PeakFound state when the preliminarilydetected event is concluded as “verified;” transitioning from theVerification state to the INIT state when verification is concluded tobe “not verified;” and staying in the Verification state whenverification is not concluded.

In a different embodiment, the system state controller 220 is furtherconfigured for: in the PeakFound state: computing the thresholdsadaptively in a third way; detecting the verified event based on thethresholds and the at least one of: the set of selected significantlocal peaks and related characteristics; detecting the excessivebackground interfering motion based on the thresholds and the remainingspectral energy; staying in the PeakFound state when the verified eventis concluded as “detected;” transitioning from the PeakFound state tothe INIT state when the verified event is concluded as “not detected”for a number of time instances; and transitioning from the PeakFoundstate to the Motion state when the excessive background interferingmotion is concluded as “detected” for a number of time instances.

In a different embodiment, the system state controller 220 is furtherconfigured for: in the Motion state: computing the thresholds adaptivelyin a fourth way; detecting the excessive background interfering motionbased on the thresholds and the remaining spectral energy; staying inthe Motion state when the excessive background interfering motion isconcluded as “detected;” and transitioning from the Motion state to theINIT state when the excessive background interfering motion is concludedas “not detected” for a number of time instances.

FIG. 3 illustrates an exemplary block diagram of a vital sign estimator,e.g. the vital sign estimator 210 in FIG. 2, for vital sign detectionand monitoring, according to one embodiment of the present teaching. Asshown in FIG. 3, the vital sign estimator 210 in this example includes achannel information processor 310, a spectral analyzer 320, an energyspectrum normalizer 330, a peak detector 340, a breathing rate estimator350, and a real-time updater 360.

In one embodiment, the channel information processor 310, the spectralanalyzer 320, and the energy spectrum normalizer 330 are configured forprocessing the at least one TSCI in the current sliding time window onboth time domain and frequency domain, while the peak detector 340 isconfigured for detecting at least one peak in the frequency domain, suchthat the current characteristics related to the pseudo-periodic motionof the object is computed based on the at least one peak in thefrequency domain.

In one embodiment, for each CI of a particular portion of the at leastone TSCI in the current sliding time window, the channel informationprocessor 310 in this example is configured for: obtaining N1 (aninteger greater than one) frequency domain components of the CI;determining a timestamp associated with the CI; and preprocessing the N1frequency domain components. The preprocessing may comprise: cleaningphases of the N1 frequency domain components, and normalizing the N1frequency domain components such that the N1 frequency domain componentshave a unity total power. In this case, the current characteristicsrelated to the pseudo-periodic motion of the object is computed based onthe preprocessed N1 frequency domain components of the CI of theparticular portion of the at least one TSCI in the current sliding timewindow.

In another embodiment, for each CI of a particular portion of the atleast one TSCI in the current sliding time window, the spectral analyzer320 in this example is configured for: converting N1 frequency domaincomponents of the channel information using an inverse frequencytransform to N2 time domain coefficients of the channel information, andretaining first C of the N2 time domain coefficients, wherein C is notlarger than N2. Each channel information is associated with a timestamp.N2 is not smaller than N1. Each of the N2 coefficients is associatedwith a time delay. The inverse frequency transform comprises at leastone of: inverse Fourier transform, inverse Laplace transform, inverseHadamard transform, inverse Hilbert transform, inverse sine transform,inverse cosine transform, inverse triangular transform, inverse wavelettransform, inverse integer transform, inverse power-of-2 transform,combined zero padding and transform, and inverse Fourier transform withzero padding. The spectral analyzer 320 in this example may beconfigured for correcting timestamps of all channel information of theparticular portion of the at least one TSCI in the current sliding timewindow so that the corrected timestamps of time-corrected channelinformation are uniformly spaced in time. The correcting the timestampsmay comprise: identifying a particular CI with a particular timestamp tobe replaced by a time-corrected CI with a corrected timestamp, andcomputing the time-corrected CI by computing the C retained time domaincoefficients of the time-corrected CI at the corrected timestamp usingan interpolation filter associated with the corrected timestamp. In thiscase, the current characteristics related to the pseudo-periodic motionof the object is computed based on the time-corrected CI of theparticular portion of the at least one TSCI in the current sliding timewindow.

The spectral analyzer 320 may be further configured for: for each of theC retained time domain coefficients: identifying a correspondingretained time domain coefficient of each CI of the particular portion ofthe at least one TSCI in the current sliding time window, applyingbandpass filtering to all the corresponding identified retained timedomain coefficients, and applying a frequency transform to all thecorresponding identified retained time domain coefficients. A length ofthe frequency transform is not smaller than a length of the portion. Inthis case, the current characteristics related to the pseudo-periodicmotion of the object is computed based on outputs of the frequencytransform applied to each corresponding retained time domain coefficientof the CI of the particular portion of the at least one TSCI in thecurrent sliding time window.

The spectral analyzer 320 may be further configured for: processing theoutputs of the frequency transform applied to each correspondingretained time domain coefficient of the CI of the particular portion ofthe at least one TSCI in the current sliding time window. The frequencytransform comprises at least one of: Fourier transform, Laplacetransform, Hadamard transform, Hilbert transform, sine transform, cosinetransform, triangular transform, wavelet transform, integer transform,power-of-2 transform, combined zero padding and transform, and/orFourier transform with zero padding. The processing comprises at leastone of: preprocessing, processing, post-processing, filtering, linearfiltering, nonlinear filtering, folding, grouping, energy computation,low-pass filtering, bandpass filtering, high-pass filtering, matchedfiltering, enhancement, restoration, de-noising, spectral analysis,inverse linear transform, nonlinear transform, feature extraction,machine learning, recognition, labeling, training, clustering, grouping,sorting, thresholding, comparison with time-corrected channelinformation of another portion of another time series of channelinformation in another sliding time window, similarity scorecomputation, vector quantization, compression, encryption, coding,storing, transmitting, representing, merging, combining, splitting,restricting to selected frequency band, and/or spectrum folding.

In one embodiment, the spectral analyzer 320 in this example isconfigured for: determining a timestamp associated with each channelinformation of the at least one TSCI; correcting timestamps of allchannel information of a particular portion of the at least one TSCI inthe current sliding time window so that the corrected timestamps oftime-corrected channel information are uniformly spaced in time; andperforming an operation on the time-corrected channel information withrespect to the corrected timestamps. In this case, the currentcharacteristics related to the pseudo-periodic motion of the object iscomputed based on an output of the operation. The operation comprises atleast one of: preprocessing, processing, post-processing, filtering,linear filtering, nonlinear filtering, low-pass filtering, bandpassfiltering, high-pass filtering, matched filtering, enhancement,restoration, de-noising, spectral analysis, frequency transform, inversefrequency transform, linear transform, nonlinear transform, featureextraction, machine learning, recognition, labeling, training,clustering, grouping, sorting, thresholding, peak detection, comparisonwith time-corrected channel information of another portion of anothertime series of channel information in another sliding time window,similarity score computation, vector quantization, compression,encryption, coding, storing, transmitting, representing, merging,combining, fusion, linear combination, nonlinear combination, and/orsplitting.

The energy spectrum normalizer 330 in this example may be configuredfor: processing the outputs of the frequency transform applied to eachcorresponding retained time domain coefficient of the CI of theparticular portion of the at least one TSCI in the current sliding timewindow, wherein the processing comprises at least one of: averaging,weighted averaging, averaging over selected frequency, averaging overselected time domain coefficients, and averaging over antenna links. Theaveraging over antenna links may be weighted averaging over the at leastone TSCI. The averaging over selected time domain coefficients may beweighted averaging over the C retained time domain coefficients.

The peak detector 340 in this example is configured for: identifying atleast one local maximum and at least one local minimum in the frequencydomain; computing at least one local signal-to-noise-ratio-like(SNR-like) parameter for each pair of a local maximum and a localminimum adjacent to each other; and identifying significant local peakseach being at least one of: a local maximum with SNR-like parametergreater than a first threshold T1 and a local maximum with an amplitudegreater than a threshold T2. The at least one local maximum and the atleast one local minimum may be identified in the frequency domain usinga persistence-based approach.

The breathing rate estimator 350 in this example is configured for:selecting a set of selected significant local peaks from the set ofidentified significant local peaks based on a selection criterion. Inthis case, the current characteristics related to the pseudo-periodicmotion of the object is computed based on the set of selectedsignificant local peaks and frequency values associated with the set ofselected significant local peaks. The breathing rate estimator 350 maybe further configured for: computing information associated with aremaining spectrum with the set of selected significant local peaksremoved; and detecting an event associated with the currentcharacteristics related to the pseudo-periodic motion of the objectbased on at least one of: a remaining spectral energy of the remainingspectrum, an adaptive threshold, and a finite state machine, where theevent comprises at least one of: a presence, an absence, an appearance,a disappearance, a steady behavior and a non-steady behavior of at leastone of: non-periodic motion, transient motion, strong motion, weakmotion, strong background interference and another periodic motion. Thereal-time updater 360 in this example is configured for updating thebreathing rate estimations generated by the breathing rate estimator 350regularly.

FIG. 4 illustrates a flowchart of an exemplary method 400 for vital signdetection and monitoring, e.g. for breathing detection and breathingrate estimation, according to one embodiment of the present teaching. Atoperation 402, a channel frequency response (CFR) acquisition isperformed, e.g. by the channel information processor 310 in FIG. 3. Inone embodiment, the channel information processor 310 acquires CFRs fromoff-the-shelf WiFi chips. For example, one can obtain 114 subcarriers ina CFR frame on a 5 GHz WiFi channel with a maximum of 20 dBmtransmission power. At operation 404, CFR sanitization is performed,e.g. by the channel information processor 310 in FIG. 3. In oneembodiment, due to the inevitable phase misalignment between the WiFitransmitter and receiver, there exist severe phase distortions in theCFRs. To remedy this issue, one can clean the CFR phase by theconventional linear regression method. One can write the cleaned CFR onsubcarrier k as G [k]. At operation 406, a CFR normalization isperformed, e.g. by the channel information processor 310 in FIG. 3. Inone embodiment, the receiving power of the CFRs is time-varying.Unfortunately, in the absence of automatic-gain-control (AGC) values onthe CFR chips adopted, the receiving power is unmeasurable. Therefore,one can normalize the CFRs across all subcarriers such that each CFR hasunit power. Although this step incurs information loss, it still leadsto much-improved performance. Here, one can write the CFRs captured ondifferent subcarriers and different time instances as a CFR matrix Gwith dimension K×N where N is the number of CFR frames collected. Thematrix is given as

$\begin{matrix}{G = \begin{bmatrix}{{G_{S_{0}}\left\lbrack t_{0} \right\rbrack}\mspace{25mu}} & {{G_{S_{0}}\left\lbrack t_{1} \right\rbrack}\mspace{25mu}} & \ldots & {{G_{S_{0}}\left\lbrack t_{N - 1} \right\rbrack}\mspace{25mu}} \\{{G_{S_{1}}\left\lbrack t_{0} \right\rbrack}\mspace{25mu}} & {{G_{S_{1}}\left\lbrack t_{1} \right\rbrack}\mspace{25mu}} & \ldots & {{G_{S_{1}}\left\lbrack t_{N - 1} \right\rbrack}\mspace{25mu}} \\{\vdots\mspace{95mu}} & {\vdots\mspace{95mu}} & {\vdots\mspace{20mu}} & {\vdots\mspace{124mu}} \\{G_{S_{K - 1}}\left\lbrack t_{0} \right\rbrack} & {G_{S_{K - 1}}\left\lbrack t_{1} \right\rbrack} & \ldots & {G_{S_{K - 1}}\left\lbrack t_{N - 1} \right\rbrack}\end{bmatrix}} & (1)\end{matrix}$where t_(i) stands for the time instance where the i-th CFR is capturedand s_(j) stands for the subcarrier index of the j-th subcarrier.

At operation 408, an inverse fast Fourier transform (IFFT) is performed,e.g. by the spectral analyzer 320 in FIG. 3. In one embodiment, one canconvert the filtered CFRs into time-domain CIRs for de-noising usingIFFT. The reason why IFFT could improve the performance lies in that:the useful signal contained in multipath components (MPC)s with a largetime delay is less significant than the MPCs with a smaller time delaymainly due to the significant attenuations relevant to the propagationdistance of EM waves. By using IFFT, one could focus on the first fewchannel taps and ignore channel taps with a large time delay. The CIR oftime t_(i) takes the form

$\begin{matrix}{{{{\underset{\_}{G}}_{n}\left\lbrack t_{i} \right\rbrack} = {\sum\limits_{k = s_{0}}^{s_{K - 1} - 1}{{G_{s_{k}}\left\lbrack t_{i} \right\rbrack}e^{j\; 2\pi\;\frac{nk}{K^{\prime}}}}}},{n = 0},1,2,\ldots\mspace{14mu},{K^{\prime} - 1}} & (2)\end{matrix}$where K′ is the size of IFFT given as 2^(ceil[log) ² ^((K)]) where ceil() means ceiling operation.

At operation 410, a timestamp correction is performed, e.g. by thespectral analyzer 320 in FIG. 3. In one embodiment, the time instancest_(i) can be written as (i−1)T_(s) where T_(s) is the CFR samplinginterval. Nevertheless, in practice, other wireless systems mightco-exist with the breathing tracking system. Due to the Carrier-sensemultiple access with collision avoidance (CSMA/CA) mechanism, thetransmission of WiFi packets are not uniformly scheduled over the air.More specifically, the WiFi devices would probe the medium beforetransmission. In case that some other WiFi devices are transmittingpackets, the other WiFi networks in the same area must remain silent,back-off for a period of time, and probe the medium again to check ifthere exists on-going WiFi traffic. Therefore, one can no longer assumethat t_(i)=(i−1)T_(s).

To resolve this problem, one can turn to the interpolation technique.One can define the exact time instances to be interpolated as [t₀,t_(N-1)] with a unit step size of T_(s). Then, one can perform a linearinterpolation onto the exact time instances based on the actualtimestamps t₀, t₁, t₂, . . . , t_(N-1). This leads to the temporalcorrected CIRs which can be written as

$\begin{matrix}{\overset{\_}{G} = \begin{bmatrix}{{\overset{\_}{G}}_{0}\left\lbrack t_{0} \right\rbrack} & {{\overset{\_}{G}}_{0}\left\lbrack t_{1} \right\rbrack} & \ldots & {{\overset{\_}{G}}_{0}\left\lbrack t_{N - 1} \right\rbrack} \\{{\overset{\_}{G}}_{1}\left\lbrack t_{0} \right\rbrack} & {{\overset{\_}{G}}_{1}\left\lbrack t_{1} \right\rbrack} & \ldots & {{\overset{\_}{G}}_{1}\left\lbrack t_{N - 1} \right\rbrack} \\\vdots & \vdots & \vdots & \vdots \\{{\overset{\_}{G}}_{K^{\prime} - 1}\left\lbrack t_{0} \right\rbrack} & {{\overset{\_}{G}}_{K^{\prime} - 1}\left\lbrack t_{1} \right\rbrack} & \ldots & {{\overset{\_}{G}}_{K^{\prime} - 1}\left\lbrack t_{N - 1} \right\rbrack}\end{bmatrix}} & (3)\end{matrix}$

At operation 412, a bandpass filtering is performed, e.g. by thespectral analyzer 320 in FIG. 3. In one embodiment, the CFRs can beanalyzed using standard spectral analysis tools. The breathing rate ofan average adult at rest ranges from 12 to 18 BPM. On the other hand,the breathing rate of an infant can be as high as 40 BPM, while thebreathing rate of elderly people ranges from 10 to 30 BPM. Therefore,one can use bandpass filtering to eliminate the high-frequency andlow-frequency noise and makes the breathing signal much more stable overtime. This lays the foundation of the spectral analysis via FFT as thenext step. For example, one can set the passband of the bandpass filteras 8 BPM to 42 BPM, which translates into 0.133 to 0.7 Hz. One can adopta 81 taps finite-impulse-response (FIR) filter, with coefficientsgenerated by MATLAB filter toolbox. The attenuation is −32 dB for firststopband ([0,8] BPM) and −40 dB for the right stopband ([42,300] BPM)(With a sounding rate of 10 Hz, the maximum resolvable frequency is 5 Hzwhich translates into 300 BPM). Denoting the filter coefficients as h₀,h₁, . . . , h_(H-1), the filtered CFR matrix can be written as

$\begin{matrix}{\overset{\sim}{G} = \begin{bmatrix}{{\overset{\sim}{G}}_{0}\left\lbrack t_{0}^{\prime} \right\rbrack} & {{\overset{\sim}{G}}_{0}\left\lbrack t_{1}^{\prime} \right\rbrack} & \ldots & {{\overset{\sim}{G}}_{0}\left\lbrack t_{N^{\prime} - 1}^{\prime} \right\rbrack} \\{{\overset{\sim}{G}}_{1}\left\lbrack t_{0}^{\prime} \right\rbrack} & {{\overset{\sim}{G}}_{1}\left\lbrack t_{1}^{\prime} \right\rbrack} & \ldots & {{\overset{\sim}{G}}_{1}\left\lbrack t_{N^{\prime} - 1}^{\prime} \right\rbrack} \\\vdots & \vdots & \vdots & \vdots \\{{\overset{\sim}{G}}_{K^{\prime} - 1}\left\lbrack t_{0}^{\prime} \right\rbrack} & {{\overset{\sim}{G}}_{K^{\prime} - 1}\left\lbrack t_{1}^{\prime} \right\rbrack} & \ldots & {{\overset{\sim}{G}}_{K^{\prime} - 1}\left\lbrack t_{N^{\prime} - 1}^{\prime} \right\rbrack}\end{bmatrix}} & (4)\end{matrix}$where t′_(i)=i×T_(s), N′ is the length of one CFR frame on a givensubcarrier expressed as N−H+1, and {tilde over (G)}_(k)[t′_(j)]represents the filtered CIR on the k-th tap, given as{tilde over (G)} _(k)[t′ _(j)]=Σ_(n=0) ^(H-1) h[n]{tilde over (G)}_(k)[t _(j-n)],j≥H−1  (5)

At operation 414, a spectral analysis via fast Fourier Transform (FFT)is performed, e.g. by the spectral analyzer 320 in FIG. 3. In oneembodiment, one can perform FFT on the CIRs for each channel tap, whichleads to K′ spectrum estimations. The energy spectrum for the k-th tapon the u-th frequency bin is given as

$\begin{matrix}{{{F_{k}\lbrack u\rbrack} = {{\sum\limits_{i = 0}^{N^{''} - 1}{{{\overset{\sim}{G}}_{n}\left\lbrack t_{i}^{\prime} \right\rbrack}e^{{- j}\; 2\pi\;\frac{iu}{N^{''\;}}}}}}^{2}},{u = {{- N^{''}}/2}},{{{- N^{''}}/2} + 1},\ldots\mspace{14mu},{{N^{''}/2} - 1.}} & (6)\end{matrix}$where N″ is the size of FFT given as 2^(ceil[log) ² ^((N′)]), and thespectral resolution is thus

${\Delta\; f} = {\frac{1}{N^{''} \times T_{s}}.}$Given that the spectrum range-of-interest is [f₁, f₂] (human breathingrates are confined in a small range), the range of frequency bins ofinterest can be written as [u₁=ceil[f₁×N″×T_(s)],u₂=floor[f₂×N″×T_(s)]]. Further taking the harmonics of breathing signalinto consideration, one can extend the frequency bin range into [−u₂,−u₁]∪[u₁, u₂]. Then, one can perform a spectrum folding which leads tothe folded energy spectrum given asF _(k)[u]=½[F _(k)[u]+F _(k)[−u]],u∈{u ₁ ,u ₁ +Δf, . . . ,u ₂ −Δf,u₂}.  (7)Assembling the spectrum on all subcarriers together leads to the matrixF:

$\begin{matrix}{F = \begin{bmatrix}{{\overset{\_}{F}}_{{- K^{\prime}}/2}\left\lbrack u_{1} \right\rbrack} & {{\overset{\_}{F}}_{{- K^{\prime}}/2}\left\lbrack {u_{1} + {\Delta\; f}} \right\rbrack} & \ldots & {{\overset{\_}{F}}_{{- K^{\prime}}/2}\left\lbrack u_{2} \right\rbrack} \\{{\overset{\_}{F}}_{{{- K^{\prime}}/2} + 1}\left\lbrack u_{1} \right\rbrack} & {{\overset{\_}{F}}_{{{- K^{\prime}}/2} + 1}\left\lbrack {u_{1} + {\Delta\; f}} \right\rbrack} & \ldots & {{\overset{\_}{F}}_{{{- K^{\prime}}/2} + 1}\left\lbrack u_{2} \right\rbrack} \\\ldots & \ldots & \ldots & \ldots \\{{\overset{\_}{F}}_{{K^{\prime}/2} - 1}\left\lbrack u_{1} \right\rbrack} & {{\overset{\_}{F}}_{{K^{\prime}/2} - 1}\left\lbrack {u_{1} + {\Delta\; f}} \right\rbrack} & \ldots & {{\overset{\_}{F}}_{{K^{\prime}/2} - 1}\left\lbrack u_{2} \right\rbrack} \\{{\overset{\_}{F}}_{K^{\prime}/2}\left\lbrack u_{1} \right\rbrack} & {{\overset{\_}{F}}_{K^{\prime}/2}\left\lbrack {u_{1} + {\Delta\; f}} \right\rbrack} & \ldots & {{\overset{\_}{F}}_{K^{\prime}/2}\left\lbrack u_{2} \right\rbrack}\end{bmatrix}} & (8)\end{matrix}$

At operation 416, an averaging over channel taps is performed, e.g. bythe energy spectrum normalizer 330 in FIG. 3. In one embodiment, sincethe energy of the breathing signal concentrates in the first few CIRtaps, one can take an average over channel taps with index falling inthe range of [0, S−1], where S is termed as the stripe width. Theaveraged energy spectrum is then given as

$\begin{matrix}{{{\underset{\_}{F}\lbrack u\rbrack} = {\frac{1}{S}{\sum\limits_{k = 0}^{S}{{\overset{\_}{F}}_{k}\lbrack u\rbrack}}}},{u \in {\left\{ {u_{1},{u_{1} + {\Delta\; f}},\ldots\mspace{14mu},{u_{2} - {\Delta\; f}},u_{2}} \right\}.}}} & (9)\end{matrix}$

At operation 418, an averaging over antenna links is performed, e.g. bythe energy spectrum normalizer 330 in FIG. 3. In one embodiment, thedisclosed system is a MIMO (multiple input multiple output) system. Assuch, there always exist multiple antennas on either end or both ends ofthe transmitter and receiver which gives rise to diversity in wirelesscommunication. Here, one may also incorporate multiple antenna links inMIMO systems to improve the overall performance of breathing tracking.For each antenna link m out of a total of M links, one can calculate thefolded energy spectrum as shown in (7) denoted as F _(m)[u], followed bycomputing the averaged energy spectrum over the available antenna linksgiven as

$\begin{matrix}{{{\overset{.}{F}\lbrack u\rbrack} = {\frac{1}{M}{\sum\limits_{m = 1}^{M}{{\underset{\_}{F}}_{m}\lbrack u\rbrack}}}},{u \in {\left\{ {u_{1},u_{2}} \right\}.}}} & (10)\end{matrix}$

For the convenience of further processing, one can transform the linearscale energy spectrum into its dB scale counterpart as{dot over (F)} _(dB)[u]=10 log₁₀ {dot over (F)}[u]  (11)

At operation 420, a peak detection is performed, e.g. by the peakdetector 340 in FIG. 3. In one embodiment, the locations of the peaks inthe energy spectrum {dot over (F)}_(dB)[u] indicate the estimatedbreathing rates. To extract these peaks, one may leverage thepersistence-based approach to obtain multiple pairs of local maximalsand local minimals. For the i-th pair of local maximum p_(max)[i] andminimum p_(min)[i], one may evaluate their difference asv_(i)=p_(max)[i]−p_(min)[i], which can be regarded as thesignal-to-noise ratio (SNR) for the i-th peak. Here, assuming a total ofU detected peaks in the energy spectrum, one may use both the peakamplitude {p_(max)[i]}_(i=1, 2, . . . , U) and the local peak SNR{v[i]}_(i=1, 2, . . . , U) as features for further peak filtering. Inparticular, one may keep those peaks with amplitudes and SNRs largerthan two thresholds, namely, the amplitude threshold p_(max) ⁰ and SNRthreshold v⁰, respectively.

At operation 422, a breathing rate estimation is performed, e.g. by thebreathing rate estimator 350 in FIG. 3. In one embodiment, assuming atotal of U′ peaks remaining after the previous step and P breathingrates to be detected, one can pick the top P out of U′ peaks and use thecorresponding peak locations {{circumflex over(f)}_(i)}_(i=1, 2, . . . , U′) measured in Hz as the breathing rateestimations for P people (When P≥U′, then one can suffer from a missdetection rate of

$\frac{P - U^{\prime}}{P}.$On the other hand, when P<U′, one can suffer from a false alarm rate of

$\left. \frac{U^{\prime} - P}{U^{\prime}} \right).$Meanwhile, one can record the spectrum energy without the peaks, denotedas F given asF _(dB)=10 log₁₀(Σ_(u=u) ₁ ^(u) ² 10^({dot over (F)}) ^(dB)^([u])−Σ_(i=1) ^(P)10^(p) ^(max) ^([i])).  (12)The metric F _(dB) is utilized to detect breathing in the finite statemachine presented in the next. The breathing rate estimations with BPMas the unit are then given as {{circumflex over(b)}_(i)}_(i=1, 2, . . . , U′) with {circumflex over(b)}_(i)=60{circumflex over (f)}_(i).

At operation 424, a real-time updating of the breathing rate estimationis performed, e.g. by the real-time updater 360 in FIG. 3. In oneembodiment, the disclosed breathing monitoring system keeps updating thebreathing rate estimations regularly. Here, one can define another twoparameters: window size C_(window) and shift size C_(shift), bothmeasured in the number of samples. The first set of breathing rateestimations are generated by performing spectral analysis on CFRs withsample index [0, C_(window)−1], and the second set of breathing rateestimations are generated by the same procedure on CFRs with sampleindex [C_(shift), C_(window)+C_(shift)−1]; the overlap between the twoadjacent windows is then given as C_(window)−C_(shift). A smallerC_(shift) leads to a prompter real-time updating rate.

In a realistic environmental setting, there always exist motions fromother people and/or objects, which introduces motion interference to thebreathing tracking system. At the same time, motions of the subjectunder monitoring also inject interference. Both types of interferenceswould significantly deteriorate the performance.

In light of this issue, one can supplement the system with afinite-state-machine (FSM), e.g. in the system state controller 220,composed of several states into the breathing monitoring system so thatthe system could automatically tune its parameters such as peakdetection thresholds under different states. In one embodiment, theremay be four different states in the FSM which are illustrated below andas shown in FIG. 5. For convenience, the transitions are shown under thesingle-person breathing monitoring case. One can denote the peak searchrange as [u₁, u₂].

First, the FSM may have an INIT state 512. This is the default statewhen the system is powered on. In this state, the system uses thedefault peak detection thresholds (p_(max) ⁰, v⁰). If for a specific CFRtime window w, it detects that p_(max)[0]≥p⁰, v[0]≥v⁰, the system wouldtune the peak detection thresholds as (p′⁰=p_(max) ⁰−Δp⁰, v′⁰=v⁰−Δv⁰).One can introduce Δp⁰ and Δv⁰ to leave margins for ensuing peakdetections. Then, the FSM switches to state Verification. On the otherhand, if F _(dB)≥p_(motion) where p_(motion) is the threshold for motiondetection, the FSM switches to the state Motion to indicate that thecurrent time window suffers from the major motion interferences.Otherwise, it stays in the current state. The output of the INIT stateis a P×1 all-zero vector implying that no breathing rate estimations canbe formulated. Also, in this state, the system restores the peak searchrange back to [u₁, u₂].

Second, the FSM may have a Verification state 514. To avoid detection offake peaks, one can use an internal counter to record how many times inconsecutive that a peak can be detected. If a peak can be detected for aconsecutive of γ time windows, then the system switches its state intostate PeakFound. In case that the peak becomes weaker, the FSM switchesto state INIT. When F _(dB) exceeds p_(motion), the FSM switches to themotion state. Otherwise, the system stays in the current state. Theoutput of this state is the P×1 breathing rate estimations. In thisstate, the system would zoom into a neighboring region of the estimatedbreathing rate. For instance, given an estimation of {circumflex over(b)} with respect to a peak location at {circumflex over(f)}={circumflex over (b)}/60, the system would select [u′₁, u′₂] where

$u_{1}^{\prime} = {{{\max\left\lbrack {u_{1},{\hat{f} - {\frac{\Delta\; S}{2} \times N^{''}T_{S}}}} \right\rbrack}\mspace{14mu}{and}\mspace{14mu} u_{2}^{\prime}} = {\min\left\lbrack {u_{2},{\hat{f} + {\frac{\Delta\; S}{2} \times N^{\prime}T_{S}}}} \right\rbrack}}$are the starting and ending index of the updated search range. Here, ΔSstands for the size of the search range.

Third, the FSM may have a PeakFound state 516. Now, the systemdetermines that an authentic peak is detected and can be used as thebreathing rate estimation. To prevent potential error propagation, thesystem no longer updates the two thresholds as shown in the Verificationstate. In case that the peak becomes weaker, the system switches back tothe INIT state. Likewise, the system would switch to the motion statefor sufficiently large F _(dB). The output of this state is the P×1breathing rate estimations. Also, the search range is updated based onthe breathing rate estimations.

Fourth, the FSM may have a Motion state 518. In this state, the systemdecides that no credible breathing rate estimation can be produced dueto the presence of strong motions. It can switch to the INIT state whenF _(dB) falls below p_(motion). The output of this state is the P×1breathing rate estimations formulated in the latest Verification orPeakFound state.

The idea of using FSM to overcome the motion interference issue underthe single-person case can be extended to the multi-person case byrunning multiple FSMs in parallel, with each FSM captures the status ofone specific person.

The FSMs associated with different people interact with each other,i.e., they are not independent. To make the FSMs more predictable, onecan define the following rules, as shown in FIG. 6.

Rule 1: FSMs operating first would always select the strongest peak inits frequency range. For example, the i-th FSM, denoted as FSM(i),selects the strongest peak in its search range [u′_(1,i),u′_(2,i)].

Rule 2: To avoid duplication of breathing rate estimations, no two FSMscan pick the same peak in each time window. For example, if FSM(i)selects peak at location {circumflex over (f)}_(i), then FSM(j), j>iwould look for other peaks not located at {circumflex over (f)}_(i).This rule may not handle the following issue: two persons breathing withdifferent breathing rates. The breathing rate of one person increasesand the other decreases. Sooner or later, these two breathing rateswould meet each other and the disclosed FSM cannot get the breathingrate of the second person (the rendezvous problem).

Rule 3: To circumvent the situation of selecting peaks encapsulating theharmonics of breathing, FSM(i) would select the peak in its search rangefirst denoted as {circumflex over (f)}_(i). Then, FSM(i) checks thepeaks selected by FSM(j), j=1, 2, . . . , i−1 to see if there exists aselected peak {circumflex over (f)}_(j) such that |2{circumflex over(f)}_(i)−{circumflex over (f)}_(j)|<ε_(harmonic) or |{circumflex over(f)}_(i)−2{circumflex over (f)}_(j)|<ε_(harmonic) where ε_(harmonic) isa predetermined threshold. If at least one such peak can be found, thenFSM(i) continues to select the next significant peak in its searchrange. Otherwise, FSM(i) uses {circumflex over (f)}_(i) as the detectedpeak, and the same procedure is repeated for the other FSMssequentially.

FIG. 6 visualizes an example of two FSMs selecting two peakssequentially. One can assume that both peaks satisfy the criterion ofthresholds. At time t₁ 602, FSM 1 searches the energy spectrum and picksthe strongest peak denoted as peak 1 602, 606. Then, FSM(2) comes in andattempts to select peak 1 602, 608. However, since the first peak isalready taken by FSM(1), FSM(2) gives up its selection and moves on topeak 2 604, 610.

In addition to the embodiments of the peak detection and FSM disclosedabove, FIG. 7 illustrates a flowchart of an exemplary method 700 forvital sign estimation, according to another embodiment of the presentteaching. As shown in FIG. 7, there may be no threshold when selectingthe peaks. As discussed above, the vital sign estimator 210 is a corepart of the breathing monitoring system. The vital sign estimator 210may perform channel state information (CSI) normalization 702, CSIsanitization 704, frequency-domain CSI to time-domain channel impulseresponse (CIR) conversion 706, bandpass filtering 708, spectrum analysis710, spectrum combining (averaging over tap and/or antenna links) 712,and peak extraction 714. Each time, one may feed N CSIs collected at Nsample epochs in the vital sign estimator 210, with N being the CSIblock size. Denoting G_(comb)′^(i) as the combined spectrum afteraveraging or weighted averaging for CSI block I, any proper peakdetector may be applied on G_(comb)′^(i) to find several local maxima716 denoted as

={

}_(i=0, 1, . . . , P-1) where

stands for the breathing rate with respect to the peaks and P is thenumber of peaks. The process may be repeated for each CSI block i.

FIG. 8 illustrates an example of FSM for single-person monitoring,according to one embodiment of the present teaching. As shown in FIG. 8,the detected peak

716 is forwarded to the control engine sequentially for i=0, 1, 2, . . ., I−1, to output the breathing rate estimation 812. The control engineis empowered by an FSM 810 which contains four states as initialization(INIT), verification entrance (VER-Enter), peak found (PF), andverification exit (VER-End). INIT is the default state where the peakdetector captures the local maxima. VER-Enter is used to evaluate thetemporal consistency of the detected peaks. PF is the state aftersuccessful verification of temporally consistent peaks producing thebreathing rate estimations. VER-Exit handles the case when the spectrumpeaks disappear due to motions either from the subject under monitoringor other people nearby.

FIG. 9 shows a Table 900 about the state transition details of the FSM810 shown in FIG. 8. The edge index for each column of Table 900 ismarked in FIG. 8. {circumflex over (b)}[i] is the output breathing ratefor block i, {dot over (b)}[i] is the internal breathing rate used byFSM, and Δb is a variable capturing the uncertainty bound forestimation. cnt₁ and cnt₂ are counters that represent the duration ofthe FSM staying in the VER-Enter and VER-Exit states while TH₁ and TH₂are the two thresholds representing how long the FSM is allowed to stayin the two states. cnt₁ increments by 1 when the current state isVER-Enter while cnt₂ increments by 1 when the current state is VER-Exit.∩ and ∪ represent the AND condition and OR condition. {tilde over (b)}is the internal variable representing the last breathing rate estimationwhen the current state is PF and the next state is another state. !(Edgei) is the negate of the condition on edge i. The proposed FSM does notuse any peak statistic such as the peak amplitudes for detection sincethe peak statistics could be very similar under cases with and withoutbreathing which is verified experimentally.

FSM for Multi-Person Monitoring:

In case of M people to be monitored simultaneously, one can run M FSMsfollowed by the estimation engine. Each FSM works independently exceptthat the estimated breathing rate from FSM m for CSI block i denoted by{circumflex over (b)}_(i) ^(m) should be different from all the otherbreathing rates and their harmonics denoted as

{vb̂_(i)^(m^(′))}_(m^(′) = 1, 2, …  , M, m^(′) ≠ m)^(v = 1, 2, …  , V)where V is the maximum harmonic order. One example is V=2.

Experimental Results:

Single-Person NLOS with an Operating Fan: In this experiment, one canturn on an electronic fan inside the same room with the subject. Sincethe fan is an electronic device with metal cover, it would reflect someEM waves and thus introduce interference to the CFRs and degrade thebreathing monitoring performance. FIG. 10 shows a comparison 1000 of theperformances with and without FSM. A breathing rate estimation of 0indicates that the system is unable to obtain a reliable breathing rate.Although using FSM does not fully recover the ground-truth breathingrate (15 BPM), the utilization of FSM still enhances the overallperformance. The accuracy with FSM is 94.48%.

FIG. 11 demonstrates the state transition 1100 of the FSM-based schemein this experiment. Different states shown in FIG. 5 are encoded asfollows: Init→0, Verification→1, PeakFound→2, Motion→3. FIG. 11 showsthat the FSM correctly reacts to the fan moving by switching to theMotion state from time to time.

FIG. 12 demonstrates the performance 1200 of the disclosed breathingmonitoring scheme when the subject stands up for five seconds and sitsdown during the measurement. Clearly, the breathing monitoring schemeovercomes the impact of such large motions and produces stable breathingrate estimations over time.

FIG. 13 illustrates an exemplary method 1300 for vital sign detectionand monitoring, according to one embodiment of the present teaching. Atoperation 1302, a wireless signal is received through a wirelessmultipath channel impacted by pseudo-periodic motion of an object in avenue. At operation 1304, at least one time series of channelinformation (TSCI) of the channel is obtained based on the wirelesssignal. At operation 1306, at least one portion of the at least one TSCIin a current sliding time window is determined to be associated with thepseudo-periodic motion of the object. At operation 1308, a currentcharacteristics related to the pseudo-periodic motion of the object iscomputed in the current sliding time window.

In one embodiment, the disclosed system can estimate breathing rate ofmore than one people simultaneously, and can count of the amount ofpeople. The system may include a Type 1 device which is a Bot includinga transmitter, a Type 2 device which is an Origin including a receiver.The receiver can extract a time series of CSI from a wireless signal andcollect CSI for a slide window (about 10-15 sec). A vital sign estimatorcan estimate the breathing rate based on the collected CSI. If thetransmitter has M antennas and the receiver has N antennas, there are MNlinks. One CSI time series is obtained for each link. All MN links areused by the system to compute the breathing rate. The vital signestimator can be located in the cloud, or in the Origin or the Type 2device for local computing. If there are K Bots, then there can be Kinstances of the vital sign estimator, each serving one Bot. Thebreathing results of each Bot may be displayed separately on a clientapp of a user smart device. Results of some or all Bots may be combinedto give combined results. For 1 Bot with one antenna and 1 Origin withone antenna (K=1) scenario, there is only 1 link (M=N=1). Each CSI hasN1 (e.g. about 128) frequency subcarriers (called components). Each CSIis actually channel frequency response (CFR). The vital sign estimatorstarts by cleaning the CSI (including phase noise) and normalizing eachCSI so that it has unity power. Then it does zero padding to get avector of length N2 (e.g. 128, a power of 2). Then it performs N2-pointIFFT to convert the N2-point CFR to N2-point time domain channel impulseresponse (CIR). The vital sign estimator then considers the slidingwindow of CSI (i.e. portion of the time series of CI). Suppose eachwindow has L CSI (e.g. for a 60 seconds window with 30 samples/sec,L=60×30=1800). The vital sign estimator performs time base correction toensure that all CSI over the sliding window are sampled at uniformsampling time. The time base correction involves resampling the CSI withsome interpolation filter. Among the N2 CIR coefficients, a number (C)of significant CIR components are identified (e.g. C=5 identified amongN2=128 CIR components). Then the vital sign estimator considers each CIRcomponent of each of the L CSI and treats it as an L-point time series.As such, there are C L-point component time series formed using the Cidentified CIR components. Then the vital sign estimator appliesbandpass filtering to each of the C component time series; and appliesL2-point FFT (e.g. L2=20148 for L=1800). Then the vital sign estimatoridentifies local maximum (i.e. peak) in the L2-point FFT domain. Thefrequency corresponding to each peak is the breathing rate of a person.If it is known that K people are present, K peaks will be identified. Ifthe amount of people is unknown, each significant peak is considered oneperson. By counting the significant peaks, the amount of people can beobtained.

FIG. 14 illustrates an exemplary block diagram of a repeating motionmonitor 1400, according to one embodiment of the present teaching. Inone embodiment, the repeating motion monitor 1400 is in a system formonitoring a repeating motion of an object in a venue. According tovarious embodiments, the object may be a life (e.g. a human, a pet, ananimal, etc.), a device (e.g. a fan, a machine, etc.), or a land; therepeating motion may represent: breathing, heartbeat, a periodic handgesture, a periodic gait, a rotation, a vibration, or an earthquake; andthe system can monitor periodic characteristics of the motion, e.g. arate or frequency of the repeating motion. In one embodiment, the systemcomprises a transmitter (e.g. the transmitter 110), a receiver (e.g. thereceiver 120) and the repeating motion monitor 1400 that is coupled toat least one of: the transmitter 110, the receiver 120, an additionaltransmitter, an additional receiver, a cloud server, a fog server, alocal server, and an edge server.

In one embodiment, the transmitter is located at a first position in thevenue and configured for transmitting a wireless signal through awireless multipath channel impacted by a repeating motion of an objectin the venue. The receiver is located at a second position in the venueand configured for: receiving the wireless signal through the wirelessmultipath channel impacted by the repeating motion of the object in thevenue, and obtaining a time series of channel information (CI) of thewireless multipath channel based on the wireless signal. The wirelesssignal may be a probe signal or an acknowledge (ACK) signal in responseto a probe signal.

The repeating motion monitor 1400 is configured for monitoring aperiodic characteristics of the repeating motion of the object based onthe time series of CI. Each CI comprises N1 components, wherein N1 is aninteger greater than 1. The repeating motion monitor may be furtherconfigured for: decomposing the time series of CI into N1 component timeseries, each respective component time series comprising a respectiveone of the N1 components of each CI, computing N1 component-feature timeseries based on the N1 component time series, each respectivecomponent-feature time series comprises a feature of the respectivecomponent of each CI, associating a current periodic characteristic ofthe repeating motion of the object with a current sliding time window,compute N1 component sliding functions, each respective componentsliding function being a respective transform of a respectivecomponent-feature time series in the current sliding time window, andmonitoring the current periodic characteristics of the repeating motionof the object based on at least one of: the N1 component slidingfunctions, and a combined sliding function computed based on the N1component sliding functions.

In one embodiment, the sliding time window for the repeating motionmonitor 1400 to monitor the periodic characteristics can be very short,e.g. 10 to 15 seconds. The sliding window length is related to theinitial setup and/or waiting time for the repeating motion monitor 1400.A shorter sliding window means a shorter initial setup time, which meansa better user experience, since the user does not need to maintain asame breathing rate in a long sliding time window for the repeatingmotion monitor 1400 to capture the breathing.

In one embodiment, each CI comprises at least one of: a channelfrequency response (CFR) comprising N1 complex frequency components eachof which is associated with a frequency, a channel impulse response(CIR) comprising N1 complex time components each of which is associatedwith a time, and a decomposition with N1 components each of which isassociated with an index. The feature comprises at least one of: phase,magnitude, real component, purely imaginary component, mean, mode,median, expected value, variance, square, cube, power, polynomial,exponential, derivative, and integration. The respective transformcomprises at least one of: Fourier transform, Sine transform, Cosinetransform, Laplace transform, wavelet transform, Hadamard transform,Hilbert transform, slant transform, sparse transform, graph-basedtransform, lowpass filtering, highpass filtering, bandpass filtering,finite-impulse-response (FIR) filtering, infinite-impulse-response (IIR)filtering, linear filtering, convolution, nonlinear function,statistical function, mean function, variance function, autocorrelationfunction (ACF), moment generating function, nonlinear filtering, signalprocessing, graph-based processing, particle filtering, integration,differentiation, first order derivative, second order derivative, highorder derivative, neural network, learning network, feature extraction,denoising, smoothing, signal enhancement, coding, encryption, mapping,remapping, vector quantization, autoregressive (AR) filtering, movingaverage (MA) filtering, autoregressive moving average (ARMA) filtering,median filtering, mode filtering, ordered statistics filtering,percentile filtering, eigen-decomposition, singular-value decomposition,orthogonal decomposition, sparse approximation, principle componentanalysis (PCA), and independent component analysis (ICA), projection,decomposition, sampling, re-sampling, random sampling, down-sampling,up-sampling, interpolation, and extrapolation. The combined slidingfunction comprises at least one of: arithmetic mean, geometric mean,harmonic mean, power mean, f-mean, linear combination, weighted mean,weighted arithmetic mean, weighted geometric mean, weighted harmonicmean, weighted truncated mean, interquartile mean, and generalized mean,of the N1 component functions.

In one embodiment, as shown in FIG. 14, the repeating motion monitor1400 includes a channel information collector 1410, a channelinformation processor 1420, a breathing signal enhancer 1430, abreathing feature extractor 1440, and a breathing rate estimator 1450.The channel information collector 1410 in this example collects timeseries of channel information (CI) 1405, e.g. channel state informationor channel frequency response, which may be the channel information 125generated by the receiver 120. According to various embodiments, therepeating motion monitor 1400 can not only monitor and estimatebreathing rate, but also characteristics of any other repeating motions.In that case, the breathing signal enhancer 1430 is a repeating signalenhancer; the breathing feature extractor 1440 is a repetition featureextractor; the breathing rate estimator 1450 is a repetition rateestimator.

The channel information processor 1420 in this example processes thetime series of CI by e.g. decomposing the time series of CI into N1component time series, each respective component time series comprisinga respective one of the N1 components of each CI, computing N1component-feature time series based on the N1 component time series,each respective component-feature time series comprises a feature of therespective component of each CI, associating a current periodiccharacteristic of the repeating motion of the object with a currentsliding time window, and computing N1 component sliding functions, eachrespective component sliding function being a respective transform of arespective component-feature time series in the current sliding timewindow. The channel information processor 1420 may further identify andremove any unallowed feature from the at least one feature. According tovarious embodiments, a feature outside an allowable range is identifiedas an unallowed feature, a feature in the allowable range is identifiedas an unallowed feature when it is greater than an adaptive thresholdthat is computed adaptively based on an adaptive background noise floor,and the adaptive background noise floor is computed based on at leastone feature outside the allowable range.

In one embodiment, the breathing signal enhancer 1430 is configured forcomputing at least one of: N1 component frequency functions based on afrequency transform of each of the N1 respective component slidingfunctions, and a combined frequency function based on the frequencytransform of the combined sliding function. The breathing featureextractor 1440 is configured for computing at least one feature of atleast one of: the N1 component frequency functions, and the combinedfrequency function. The breathing rate estimator 1450 is configured formonitoring at least one of: the current periodic characteristics of therepeating motion of the object, and a current periodic characteristicsof an additional repeating motion of an additional object in the venue,based on the at least one feature.

In one embodiment, the breathing feature extractor 1440 is configuredfor computing at least one feature of at least one of: the combinedsliding function, and each of the N1 component sliding functions,wherein the at least one feature comprises at least one of: an extremum,a local maximum, a local minimum, a zero-crossing, a local maximumderivative, a local maximum high-order derivative, a local minimumderivative, a local minimum high-order derivative, a local zeroderivative, a positive quantity, a negative quantity, a significantquantity, a positive maximum, a positive minimum, a negative maximum, anegative minimum, a first maximum, a first minimum, a second maximum, asecond minimum, an N-th maximum, an N-th minimum, a significant maximum,a significant minimum, a significant derivative, and a significanthigh-order derivative. The breathing rate estimator 1450 is configuredfor monitoring the current periodic motion of the object based on atleast one of: the at least one feature of at least one of: the combinedsliding function and each of the N1 component sliding functions, and atleast one domain value associated with the at least one feature of atleast one of: the combined sliding function and each of the N1 componentsliding functions.

In another embodiment, the breathing feature extractor 1440 isconfigured for computing at least one dominant periodic feature of eachof the N1 component-feature time series in the current sliding timewindow, wherein the at least one dominant periodic feature comprises atleast one of: a spectral extremum, a local spectral maximum, a localspectral minimum, a spectral zero-crossing, a local maximum spectralderivative, a local maximum spectral high-order derivative, a localminimum spectral derivative, a local minimum spectral high-orderderivative, a local zero spectral derivative, a positive spectralquantity, a negative spectral quantity, a significant spectral quantity,a positive spectral maximum, a positive spectral minimum, a negativespectral maximum, a negative spectral minimum, a first spectral maximum,a first spectral minimum, a second spectral maximum, a second spectralminimum, an N-th spectral maximum, an N-th spectral minimum, asignificant spectral maximum, a significant spectral minimum, asignificant spectral derivative, and a significant spectral high-orderderivative. The breathing rate estimator 1450 is configured formonitoring the current periodic characteristics of the repeating motionof the object based on at least one of: the dominant periodic featuresof the N1 component-feature time series in the current sliding timewindow, and at least one combined dominant periodic feature based on thedominant periodic features of the N1 component-feature time series inthe current sliding time window.

In yet another embodiment, the breathing feature extractor 1440 isconfigured for computing at least one dominant periodic feature of eachof the N1 component time series in the current sliding time window,wherein the at least one dominant periodic feature comprises at leastone of: a spectral extremum, a local spectral maximum, a local spectralminimum, a spectral zero-crossing, a local maximum spectral derivative,a local maximum spectral high-order derivative, a local minimum spectralderivative, a local minimum spectral high-order derivative, a local zerospectral derivative, a positive spectral quantity, a negative spectralquantity, a significant spectral quantity, a positive spectral maximum,a positive spectral minimum, a negative spectral maximum, a negativespectral minimum, a first spectral maximum, a first spectral minimum, asecond spectral maximum, a second spectral minimum, an N-th spectralmaximum, an N-th spectral minimum, a significant spectral maximum, asignificant spectral minimum, a significant spectral derivative, asignificant spectral high-order derivative, and another periodicfeature. The breathing rate estimator 1450 is configured for monitoringthe periodic characteristics of the repeating motion of the object basedon at least one of: the dominant periodic features of the N1 componenttime series in the current sliding time window, and at least onecombined dominant periodic feature based on the dominant periodicfeatures of the N1 component time series in the current sliding timewindow.

The breathing rate estimator 1450 generates an estimation of a periodiccharacteristics (e.g. a breathing rate estimation 1455) of the repeatingmotion of the object. In one embodiment, the breathing rate estimator1450 may further compute at least one analytics based on the periodiccharacteristics of the repeating motion of the object, and transmitinformation of the periodic characteristics of the repeating motion ofthe object to a user interface. The user interface may be configured forpresenting a summary of the periodic characteristics of the repeatingmotion of the object based on the transmitted information. The summarymay comprise at least one of: an analytics, a selected time window, asubsampling, a transform, a projection, etc. The presenting may comprisepresenting at least one of: a monthly view, a weekly view, a daily view,a simplified view, a detailed view, a cross-sectional view, a smallform-factor view, a large form-factor view, a color-coded view, acomparative view, a summary view, an animation, a web view, a voiceannouncement, and another presentation related to the periodiccharacteristics of the repeating motion.

In one embodiment, the breathing rate estimator 1450 is configured for:processing the periodic characteristics of the repeating motion of theobject; computing a time function based on the periodic characteristicsof the repeating motion of the object; analyzing at least one of: theperiodic characteristics of the repeating motion of the object, and thetime function; and computing at least one analytics based on at leastone of: the periodic characteristics of the repeating motion of theobject, and the time function. The processing may comprise at least oneof: doing nothing, zero-padding, time-domain processing, frequencydomain processing, time-frequency processing, spatially varyingprocessing, temporally varying processing, adaptive processing,de-noising, smoothing, conditioning, enhancement, restoration, featureextraction, weighted averaging, averaging over antenna links, averagingover selected frequency, averaging over selected components,quantization, vector quantization, filtering, linear filtering,nonlinear filtering, low-pass filtering, bandpass filtering, high-passfiltering, median filtering, ranked filtering, quartile filtering,percentile filtering, mode filtering, linear filtering, nonlinearfiltering, finite impulse response (FIR) filtering, infinite impulseresponse (IIR) filtering, moving average (MA) filtering, auto-regressive(AR) filtering, auto-regressive moving average (ARMA) filtering,thresholding, soft thresholding, hard thresholding, soft clipping, localmaximization, local minimization, optimization of a cost function,neural network, machine learning, supervised learning, unsupervisedlearning, semi-supervised learning, transformation, mapping, transform,inverse transform, integer transform, power-of-2 transform, realtransform, floating-point transform, fixed-point transform, complextransform, fast transform, Fourier transform, Laplace transform,Hadamard transform, Hilbert transform, sine transform, cosine transform,triangular transform, wavelet transform, transformation, decomposition,selective filtering, adaptive filtering, derivative, first orderderivative, second order derivative, higher order derivative,integration, zero crossing, indicator function, absolute conversion,convolution, multiplication, division, another transform, anotherprocessing, another filter, another function, normalization, temporalnormalization, frequency normalization, magnitude correction, phasecorrection, phase cleaning, cleaning a phase associated with the channelinformation, normalizing components associated with the channelinformation, cleaning a phase of frequency components of the channelinformation, normalizing the frequency components, re-sampling,labeling, tagging, training, sorting, grouping, folding, thresholding,matched filtering, spectral analysis, clustering, quantization, vectorquantization, time correction, time base correction, time stampcorrection, sampling rate up-conversion/down-conversion, upsampling,interpolation, intrapolation, extrapolation, subsampling, decimation,compression, expansion, encryption decryption, coding, storing,retrieving, transmitting, receiving, representing, merging, combining,splitting, tracking, monitoring, projection, orthogonal projection,non-orthogonal projection, over-complete projection, decomposition,eigen-decomposition, principal component analysis (PCA), sparseapproximation, matching pursuit, and another operation. Any thresholdsmay be pre-determined, adaptively determined and/or determined by afinite state machine. The adaptive determination may be based on time,space, location, antenna, path, link, state, battery life, remainingbattery life, available power, available computational resources,available network bandwidth, etc.

In another embodiment, the breathing rate estimator 1450 is configuredfor: estimating a missing periodic characteristics of the repeatingmotion of the object based on at least one of: available periodiccharacteristics of the repeating motion of the object computed based onthe time series of CI, a different characteristics computed based on thetime series of CI, and a different characteristics computed based on adifferent time series of CI of the wireless multipath channel.

In yet another embodiment, the breathing rate estimator 1450 isconfigured for: computing a trend function by lowpass filtering a timefunction of the periodic characteristics of the repeated motion of theobject; analyzing at least one of: the time function, the trendfunction, and a detrended function computed by subtracting the trendfunction from the time function of the periodic characteristics; andmonitoring the repeating motion of the object based on at least one of:the time function, the trend function, and the detrended function.

The object may be a life (e.g. a human, a baby). The breathing rateestimator 1450 may be configured to monitor sleeping of the human.Various stages of sleeping including rapid-eye-movement (REM), non-REM,awake, asleep, interruption may be identified. At least one analyticsassociated with the sleeping stages may be computed. Analyticsassociated with sleeping quality may be computed.

The object may be a device. The breathing rate estimator 1450 may beconfigured to monitor a repeated motion such as vibration, rotation,back-and-forth action, and/or repeated machine action. Analyticsassociated with normal and/or abnormal operation of the device may becomputed. Analytics associated with location of machine parts in therepeated cycle may be computed.

The object may be land. The breathing rate estimator 1450 may beconfigured to monitor signs of earthquakes, aftershocks, shaking, and/orvibration.

In one embodiment, the system can measure more than one breathing ratesimultaneously. In this case, the repeating motion monitor 1400 isfurther configured for: monitoring an additional periodiccharacteristics of an additional repeating motion of an additionalobject contemporaneously based on the time series of CI, wherein thewireless multipath channel is further impacted by the additionalrepeating motion of the additional object in the venue. The at least onefeature comprises: a first feature associated with the periodiccharacteristics of the repeating motion of the object, and a secondfeature associated with the periodic characteristics of the additionalrepeating motion of the additional object. The periodic characteristicsof the repeating motion of the object and the periodic characteristicsof the additional repeating motion of the additional object aremonitored contemporaneously based on the first feature and the secondfeature.

In one embodiment, the breathing rate is monitored in a series ofoverlapping time window. In this case, the repeating motion of theobject is locally repeating such that, in each of a series ofoverlapping time windows, the locally repeating motion resembles aperiodic motion; and the periodic characteristics of the locallyrepeating motion of the object is monitored in each of the series ofoverlapping time window.

In another embodiment, the system can perform not only the breathingrate detection and estimation, but also other detections and estimationsfor, e.g. fall-down detection, tracking monitoring, etc. In this case,the repeating motion monitor 1400 is further configured for: performinga joint analysis on the periodic characteristics of the repeating motionof the object and at least one of: a different characteristics computedbased on the time series of CI, and a different characteristics computedbased on a different time series of CI of the wireless multipathchannel; and computing at least one joint analytics based on the jointanalysis.

FIG. 15 illustrates a flowchart of an exemplary method 1500 forestimating and monitoring a vital sign (e.g. breathing rate, heartrate), according to one embodiment of the present teaching. First,channel state information (CSI) or channel information (CI, e.g. CSI)may be obtained at operatoin 1502 by channel estimation after a radioreceiver (e.g. wireless, RF, WiFi, LTE, UWB, etc) receives a wirelesssignal (e.g. a channel probing radio signal, a reply signal, anacknowledgement signal, a data signal, or a control signal) from a radiotransmitter. The CSI/CI is preprocessed at operatoin 1504, e.g., toremove noise and/or to calculate some feature function (e.g.autocorrelation function (ACF)) from the breathing signal. Sincebreathing signal may be very weak, the received signal may be enhancedat operatoin 1506 to boost the signal-to-noise ratio (SNR) through someoperation, e.g., maximal ratio combining (MRC). The ACF may exhibit aperiodic behavior since breathing is a periodic process, and featuresabout breathing can be extracted at operation 1508 from the ACF, e.g.,detecting a (first) local maximum from the ACF. The time lag of thefirst local maximum correspond to the period T of breathing, and thenthe breathing rate estimation can be obtained at operation 1510, e.g. by60/T breath per minute.

FIG. 16 illustrates exemplary autocorrelation functions 1600 of thereceived signals under different scenarios, according to one embodimentof the present teaching. As shown in FIG. 16, when there is a breathingsignal, the ACF will exhibit a definite peak at a certain delay(although the peak value may differ over different subcarriers),contributed by the periodic breathing motions. On the contrary, nocertain peaks can be observed on any subcarrier when there is nobreathing (i.e., no periodic motions). In principle a time delayslightly longer than one breathing cycle (e.g., 5 to 7 seconds) issufficient to pick up the first breathing rate and later oninstantaneous estimates can be produced every one second.

Motion Statistics:

In prior to breathing estimation, a key step is to examine whether thereexists detectable breathing signal (i.e., whether breathing existswithout extra big motions). As mentioned previously, breathing willeasily be buried in other large body motions, and should not beestimated if there is none. One can also investigate the property of theACF for this purpose since, in addition to breathing, the calculated ACFactually offers indicators for detection of arbitrary motions.

Breathing Detection and Estimation:

Based on the calculated ACF and its inherent characteristics, the systemfirst detects the presence and absence of breathing and, if presented,then estimates the breathing rate, accurately and instantaneously. FIG.17 illustrates exemplary features 1700 extracted from the derivedautocorrelation functions for breathing detection and estimation,according to one embodiment of the present teaching. As shown in FIG.17, for a subcarrier with frequency f, one can extract several featuresfrom {circumflex over (ρ)}_(G)(τ, f) for breathing detection, inaddition to the motion statistics. The first feature is Peak Prominence,which is the vertical distance between the peak value and the largestheight of the adjacent valleys, which measures the likelihood of theexistence of the peak. The second feature is Peak Width, which is thehorizontal distance between the two adjacent valleys, which alsomeasures the likelihood of the existence of the peak. The third featureis Peak Amplitude, which is the height of the peak, which measures theamplitude of the ACF of the breathing signal and will be comparable tothe value of motion statistic in presence of only breathing motion. Thefourth feature is Motion Interference Ratio, which is the ratio betweenthe motion statistics and peak amplitude, which measures degree of theinterference of the non-breathing motion, such as body movements,walking, standing up, typing keyboard, etc., in the environment. Thefifth feature is Peak Location, which is the horizontal distance betweenthe origin and the peak (i.e., time lags), which measures the breathingcycle. Other features may be computed.

In general, the larger the motion statistics, peak prominence, peakwidth, peak amplitude and the smaller the motion interference ratio, themore likely for the presence of the breathing signal. In one embodiment,the above features are jointly fused to claim the existence of breathingsignal and the corresponding breathing rate. Once there is a breathingsignal, the breathing rate can be estimated as BR=60/{circumflex over(τ)} breath per minute, where {circumflex over (τ)} is the location(i.e., time lags) of the first dominant peak of {circumflex over(ρ)}_(G)(τ, f).

In practice, the SNR of the breathing signal on each subcarriermodulated by minute breathing motions is very low, especially when theperson being monitored is far away from the link, covered by quilts, orbehind the wall, resulting in limited coverage and vulnerable estimationthat prevents the applications of the existing RF-based approaches.Previous approaches attempt to select a best subcarrier among others, orto average over all to improve breathing signal do not produce reliable,not to mention optimal results.

To boost the breathing SNR, the system may combine the breathing signalsmeasured on different subcarriers in the optimal way. In one embodiment,the disclosed design is based on Maximal Ratio Combining (MRC), aclassical diversity combining method in telecommunications that maximizesignal SNR by optimally combining received signals on multiple antennas.

MRC on Breathing Signal.

In the context of breathing estimation with CSI, the breathing signalb(t) denotes a periodic stationary breathing signal with zero mean,which is related to the movement of the chest and abdomen, and ismeasured by multiple subcarriers. Let g(f) and Δt_(f) stand for the gainand the random initial phase of the breathing signal measured at thefrequency f, respectively. The SNR of the breathing signal, denoted asγ(f), measured on subcarrier with frequency f at time t is defined as

$\begin{matrix}{{{\gamma(f)} = {\frac{\left\lbrack \left( {{g(f)}{b\left( {t - {\Delta\; t_{f}}} \right)}} \right)^{2} \right\rbrack}{\left\lbrack {ɛ^{2}\left( {t,f} \right)} \right\rbrack} = \frac{{g^{2}(f)}\left( {b^{2}\left( {t - {\Delta\; t_{f}}} \right)} \right)}{\sigma^{2}(f)}}},} & (13)\end{matrix}$where

[⋅] stands for the expectation operator and ε(t, f) is defined as thenoise term. For convenience, the average power of the breathing signalb(t) is normalized to unit power by definition, that is,

[b²(t)]=1, and thus one can have γ(f)=g²(f)/σ⁻²(f).

Defining

${k(f)}\overset{\Delta}{=}\frac{g^{2}(f)}{{g^{2}(f)} + {\sigma^{2}(f)}}$as the normalized channel gain, it can reflect the strength of totalmotion existing in the monitored area. The SNR of the ACF of eachsubcarrier can be estimated as N{circumflex over (k)}²(f), where N isthe number of samples used in the estimation of the ACF. Then the weightcan be set to {circumflex over (ρ)}_(G)(τ=1/F_(s), f), the ACF of G(t,f), where G(t, f) is the power response (i.e., the square of themagnitude of the channel frequency response H(t, f) at time t andfrequency/subcarrier f).

FIG. 18 illustrates an exemplary scheme 1800 for breathing signalextraction and maximization, according to one embodiment of the presentteaching. The left part of the FIG. 18 shows the decomposition of themeasured ACF of the channel power response when a person breathesnormally in the monitored area, while the right part shows the MRCscheme for boosting the SNR of the ACF of the breathing signal.

FIG. 19 illustrates an exemplary procedure of multi-person breathingrate estimation, according to one embodiment of the present teaching.The input signal 1910 ρ(τ), where 0

τ

T, denotes a combined ACF breathing signal, which is the ACF of thebreathing signal after the MRC.

Spectrum estimation is performed at operation 1920 by calculating thepower spectral density S(f), where 0

f

F, of the breathing signal ρ(τ). The spectrum estimation can be based onnon-parametric methods, e.g., periodogram algorithm, or parametricspectrum estimators, e.g., MUSIC algorithm.

Estimation of noise power is performed at operation 1930. Let fl and fhdenote the lower and upper bounds of the breathing rate of interests.The noise power n is estimated by calculating the average of the energyof the power spectral density outside the breathing range, i.e.,

${n = {{\frac{1}{T + f_{l} - f_{h}}{\sum\limits_{f \leq f_{l}}{S(f)}}} + {\sum\limits_{f \geq f_{h}}{{S(f)}.}}}}\;$

Peak detection is performed at operation 1940 by detecting all the peaksof S(f) within the range fl

f

fh, where a peak is defined as the point whose value is large than itsneighbors. Let fi denote the i-th peak. In one embodiment, after theoperation 1920, the process can go directly to the operation 1940without perform the operation 1930.

At operation 1950, it is determined whether there is any additionalpeak. Each peak, e.g. the i-th peak, is examined, e.g. by comparing itsvalue S(fi) with the estimated noise power n. If the difference islarger than a preset threshold η, i.e., S(fi)

η, then the peak is a valid peak and the corresponding breathing rateestimate is 60*fi. The process then goes to operation 1960 to update thenumber of people by adding 1 and the process goes back to operation 1950to detect any additional peak. Otherwise, if the difference is notlarger than a preset threshold, the peak is discarded. The number ofpeople detected stays the same and the process stays at operation 1950to detect any additional peak. When it is determined at operation 1950that there is no additional peak, the process ends at operation 1970.

In one embodiment, the disclosed system may include a Type 1 devicewhich is a Bot including a transmitter, a Type 2 device which is anOrigin including a receiver, and the repeating motion monitor 1400. Thereceiver can extract a time series of CSI from a wireless signal andcollect CSI for a slide window (about 10-15 sec). The repeating motionmonitor 1400 can estimate the breathing rate based on the collected CSI.If the transmitter has M antennas and the receiver has N antennas, thereare MN links. One CSI time series is obtained for each link. All MNlinks are used by the system to compute the breathing rate. Therepeating motion monitor 1400 can be located in the cloud, or in theOrigin or the Type 2 device for local computing. If there are K Bots,then there can be K instances of the repeating motion monitor 1400, eachserving one Bot. The breathing results of each Bot may be displayedseparately on a client app of a user smart device. Results of some orall Bots may be combined to give combined results. For 1 Bot with oneantenna and 1 Origin with one antenna (K=1) scenario, there is only 1link (M=N=1). Each CSI has N1 (e.g. about 128) frequency subcarriers(called components). As the phase of each subcarrier tends to be noisy,the repeating motion monitor 1400 can extract a noise-robust feature,e.g. magnitude square (i.e. square of magnitude, no phase) for eachsubcarrier. As each subcarrier is associated with a unique frequency,the feature is also associated with the same frequency. For a timeseries of CSI, the repeating motion monitor 1400 treats a respectivefeature associated with a respective frequency as a feature time series(called component-feature time series). Thus there are N1component-feature time series. It computes autocorrelation function(ACF) for each component-feature time series (within the sliding windowof about 10-15 sec). This is repeated for all frequency. The repeatingmotion monitor 1400 then computes an overall ACF as a weighted averageof ACF of all frequency components. The weights are determinedadaptively using an existing method called maximal ratio combining(MRC). Finally, the repeating motion monitor 1400 identifies the firstpositive significant local maximum. The time lag (T) associated with theidentified local max gives the breathing rate (=1/T). When there aremultiple persons, each person should have a corresponding peak in theoverall ACF. But these peaks may be close to each other, which makes ithard to tell whether there is one peak or two or three, etc.

FIG. 20 illustrates an exemplary diagram of a device 2000 in a motionmonitoring system, according to one embodiment of the present teaching.The device 2000 is an example of a device that can be configured toimplement the various methods described herein. According to variousembodiments, the device may be: a Type 1 device which is a Bot includinga transmitter, a Type 2 device which is an Origin including a receiver,the vital sign estimator 210, the system state controller 220, therepeating motion monitor 1400, and/or other components in FIGS. 1-19 and21-26. As shown in FIG. 20, the device 2000 includes a housing 2040containing a processor 2002, a memory 2004, a transceiver 2010comprising a transmitter 2012 and a receiver 2014, a synchronizationcontroller 2006, a power module 2008, and an operation module 2009.

In this embodiment, the processor 2002 controls the general operation ofthe device 2000 and can include one or more processing circuits ormodules such as a central processing unit (CPU) and/or any combinationof general-purpose microprocessors, microcontrollers, digital signalprocessors (DSPs), field programmable gate array (FPGAs), programmablelogic devices (PLDs), controllers, state machines, gated logic, discretehardware components, dedicated hardware finite state machines, or anyother suitable circuits, devices and/or structures that can performcalculations or other manipulations of data.

The memory 2004, which can include both read-only memory (ROM) andrandom access memory (RAM), can provide instructions and data to theprocessor 2002. A portion of the memory 2004 can also includenon-volatile random access memory (NVRAM). The processor 2002 typicallyperforms logical and arithmetic operations based on program instructionsstored within the memory 2004. The instructions (a.k.a., software)stored in the memory 2004 can be executed by the processor 2002 toperform the methods described herein. The processor 2002 and the memory2004 together form a processing system that stores and executessoftware. As used herein, “software” means any type of instructions,whether referred to as software, firmware, middleware, microcode, etc.which can configure a machine or device to perform one or more desiredfunctions or processes. Instructions can include code (e.g., in sourcecode format, binary code format, executable code format, or any othersuitable format of code). The instructions, when executed by the one ormore processors, cause the processing system to perform the variousfunctions described herein.

The transceiver 2010, which includes the transmitter 2012 and receiver2014, allows the device 2000 to transmit and receive data to and from aremote device (e.g., an Origin or a Bot). An antenna 2050 is typicallyattached to the housing 2040 and electrically coupled to the transceiver2010. In various embodiments, the device 2000 includes (not shown)multiple transmitters, multiple receivers, and multiple transceivers. Inone embodiment, the antenna 2050 is replaced with a multi-antenna array2050 that can form a plurality of beams each of which points in adistinct direction. The transmitter 2012 can be configured to wirelesslytransmit signals having different types or functions, such signals beinggenerated by the processor 2002. Similarly, the receiver 2014 isconfigured to receive wireless signals having different types orfunctions, and the processor 2002 is configured to process signals of aplurality of different types.

In one embodiment, the device 2000 may be a Bot or an Origin of a motionmonitoring system. The motion monitoring system may comprise at leastone Bot and at least one Origin. The synchronization controller 2006 inthis example may be configured to control the operations of the device2000 to be synchronized or un-synchronized with another device, e.g.another Origin or another Bot. In one embodiment, each of the device2000 and other Bots or Origins in the system may transmit or receive thewireless signals individually and asynchronously.

The operation module 2009 in this example may perform one or moreoperations for vital sign detection and monitoring. The operation module2009 may comprise one or more sub-modules to implement different methodsdisclosed herein. In one embodiment, the device 2000 may be the vitalsign estimator 210 of a motion monitoring system, where the operationmodule 2009 includes one or more of the components shown in FIG. 3. Inanother embodiment, the device 2000 may be the repeating motion monitor1400 of a motion monitoring system, where the operation module 2009includes one or more of the components shown in FIG. 14.

The power module 2008 can include a power source such as one or morebatteries, and a power regulator, to provide regulated power to each ofthe above-described modules in FIG. 20. In some embodiments, if thedevice 2000 is coupled to a dedicated external power source (e.g., awall electrical outlet), the power module 2008 can include a transformerand a power regulator.

The various modules discussed above are coupled together by a bus system2030. The bus system 2030 can include a data bus and, for example, apower bus, a control signal bus, and/or a status signal bus in additionto the data bus. It is understood that the modules of the device 2000can be operatively coupled to one another using any suitable techniquesand mediums.

Although a number of separate modules or components are illustrated inFIG. 20, persons of ordinary skill in the art will understand that oneor more of the modules can be combined or commonly implemented. Forexample, the processor 2002 can implement not only the functionalitydescribed above with respect to the processor 2002, but also implementthe functionality described above with respect to the operation module2009. Conversely, each of the modules illustrated in FIG. 20 can beimplemented using a plurality of separate components or elements.

In one embodiment, the present teaching discloses a method, apparatus,and a system for sleep monitoring. The disclosed method comprises:obtaining a time series of channel information (CI) of a wirelessmultipath channel using a processor, a memory communicatively coupledwith the processor and a set of instructions stored in the memory, andmonitoring the sleep-related motion of the user based on the time seriesof CI.

The time series of CI is extracted from a wireless signal transmittedbetween a Type 1 heterogeneous wireless device and a Type 2heterogeneous wireless device in a venue through the wireless multipathchannel. The wireless multipath channel is impacted by a sleep-relatedmotion of a user in the venue.

Monitoring the sleep-related motion comprises monitoring at least one ofthe following of the user: sleep timings, sleep durations, sleep stages,sleep quality, sleep apnea, sleep problems, sleep disorders, breathingproblems, gasping, choking, teeth-grinding, pause of sleep, absence ofsleep, insomnia, restlessness during sleep, hypersomnia, parasomnia,day-time sleepiness, sleep locations, sleep-while-driving, sleepdisruptions, nightmares, night terrors, sleep walking, REM sleepbehavior disorder, Circadian rhythm disorder, non-24-hour sleep-wakedisorder, periodic limb movement disorder, shift-work sleep disorder,narcolepsy, confusional arousals, sleep paralysis, another sleep-relatedcondition, and/or another sleep-related behavior.

Sleep timings comprises timings of at least one of: go-to-bed,sleep-onset, wake-up, REM-onset, NREM-onset, onset of sleep stagetransitions, sleep disorders, sleep problems, breathing problems,insomnia, hypersomnia, parasomnia, sleep hypnogram-related events, sleepdisruptions, sleep apnea, snoring during sleep, sleeping-not-on-a-bed,day-time sleep, sleep-walking, sleep-related events, sleep-relatedcondition, and/or, sleep-related behavior, etc.

Sleep stages comprises at least one of: wake-up, rapid-eye-movement(REM) and/or non-REM (NREM).

At least one of: a time function of breathing rate, and a time functionof motion statistics, of the user may be computed based on the series ofCI. If breathing is not detected at time t, the breathing rate at time tmay be computed as zero. The sleep-related motion of the user may bemonitored based on at least one of: the time function of breathing rate,and/or the time function of motion statistics, of the user.

At least one of: a time function of breathing ratio, and a time functionof motion ratio, of the user may be computed based on the series of CI.The breathing ratio at time t may be computed as percentage of time whenthe time function of breathing rate is non-zero in a first time windowcomprising the time t. The motion ratio at time t may be computed aspercentage of time when the time function of motion statistics is largerthan a first threshold within a second time window comprising the timet. The sleep-related motion of the user may be monitored based on atleast one of: the time function of breathing ratio, and/or the timefunction of motion ratio, of the user.

A sleep stage may be classified as “awake” if at least one of: themotion ratio is greater than a second threshold, and/or the breathingratio is less than a third threshold. The sleep stage may be classifiedas “asleep” if at least one of: the motion ratio is less than the secondthreshold, and/or the breathing ratio is greater than the thirdthreshold. The “asleep” stage may comprise at least one of:rapid-eye-movement (REM) stage, and/or non-REM (NREM) stage.

A breathing rate trend function may be computed by low-pass filteringthe time function of breathing rate. A detrended breathing rate functionmay be computed by subtracting the breathing rate trend function fromthe time function of breathing rate. A time function of breathing ratevariance may be computed by computing variance of the detrendedbreathing rate function within a sliding time window. The sleep-relatedmotion of the user may be monitored based on the time function ofbreathing rate variance.

An average NREM breathing rate may be computed by identifying a peak ofa histogram of the time function of breathing rate in “asleep” stage inan overnight period. (e.g. the whole night, or the whole nightsubtracting any “awake” periods). A time function of breathing ratedeviation may be computed by computing a distance between the averageNREM and a percentile of the breathing rate within a sliding timewindow. The sleep stage may be classified as at least one of: REM stageand/or NREM stage, based on the time function of breathing ratedeviation.

A time function of breathing rate variance may be computed by computingvariance of a detrended breathing rate function within a first slidingtime window. A time function of breathing rate deviation may be computedby computing a distance between an average NREM and a percentile of thebreathing rate within a second sliding time window. The sleep stage maybe classified as at least one of: REM stage, and/or NREM stage, based onthe time function of breathing rate variance and the time function ofbreathing rate deviation.

A classifier may be trained based on at least one of: breathing ratevariance, and breathing rate deviation, using machine learning. Themachine learning may comprise at least one of: supervised learning,unsupervised learning, semi-supervised learning, active learning,reinforcement learning, support vector machine, deep learning, featurelearning, clustering, regression, and/or dimensionality reduction. Thesleep stage may be classified as at least one of: REM stage, and/or NREMstage, based on the classifier.

A quantity related to the sleep-related motion of the user may becomputed based on the time series of CI. The sleep-related motion of theuser may be monitored based on the quantity.

The quantity may comprise at least one of: the time the user goes tobed, the time the user gets out of bed, the sleep onset time, total timeit takes the user to fall asleep, the wake up time, sleep disruptiontime, number of sleep disruption period, mean disruption duration,variance of disruption duration, total time in bed, total time the useris asleep, time periods of REM, time periods of NREM, time periods ofawake, total time of REM, total time of NREM, number of REM periods,number of NREM periods, time of toss and turn in bed, duration oftossing and turning, hypnogram, periods of apnea, periods of snore,total duration of apnea, number of apnea periods, average duration ofapnea period, periods of breathing problems, sleep quality score,daytime sleep, time periods of daytime sleep, total duration of daytimesleep, number of period of daytime sleep, average duration of period ofdaytime sleep, and another quantity.

As discussed above, FIG. 18 summarizes a proposed scheme for breathingsignal extraction and maximization. The left part of FIG. 18 shows thedecomposition of the measured ACF of the channel power response when aperson breathes normally in the monitored area, and the right part showsthe MRC scheme for boosting the SNR of the ACF of the breathing signal.FIG. 21 depicts an illustrative example based on real-worldmeasurements, where the SNR of the breathing signal is amplified by 2.5dB compared to the best subcarrier indicated by largest variance and by3.7 dB compared to directly averaging all subcarriers. FIG. 22 furtherdemonstrates the gains of the disclosed ACF-based MRC scheme andconfirms the observations herein that amplitudes and their variances arenot effective metrics for subcarrier selection. As seen, the subcarrierthat is the most sensitive to motion (i.e., holding the largest motionstatistic) could experience very small amplitude and low variance.

Sleep Stage Recognition

SMARS divides the continuous motion and breathing estimates of overnightsleep into 300-second epochs. For each epoch, SMARS recognizes threedifferent sleep stages, i.e., wake, REM sleep and NREM sleep. Thestaging is performed in two steps: First, SMARS differentiates wake fromsleep mainly by body motions; Second, REM and NREM stages are furtheridentified during sleep period.

Sleep/Wake Detection.

SMARS first implements a sleep-wake detector to identify the sleep andwake states. The key insight is that, more frequent body movements willbe observed when a subject is awake, while mainly breathing motionpresents when he/she is asleep. Since bodily movements are significantlystronger than breathing motions, and both of them can be easily capturedand quantified by the motion statistic defined herein, SMARS utilizes itto distinguish between sleep and wake states.

Specifically, one can define motion ratio as the percentage of time whenthe motion statistic, {circumflex over (ρ)}_(b)(1/F_(s)), is larger thana preset threshold. Thus for the wake state, a higher motion ratio isexpected, as shown in FIG. 23A. Similarly, one can also define breathingratio as the percentage of time when the breathing signal is detected.Since bodily movements destroy the periodicity of the environmentaldynamics, the breathing ratio will be lower when a subject is awake, asshown in FIG. 23B.

Combining the above two features, SMARS identifies an epoch as sleeponly when the motion ratio is smaller than the predefined threshold andthe breathing ratio is larger than the other threshold. Both thresholdsare empirically determined as in FIG. 23A and FIG. 23B. Since thedisclosed model statistically considers all multipaths indoors, thevalues of both thresholds generalize to different environments andsubjects.

REM/NREM Recognition.

SMARS exploits the following clinical facts and accordingly extracts twodistinctive features from breathing rate estimates for REM/NREM stagesclassification: Breathing rate is usually faster and presents highervariability and irregular patterns for REM stage, while more stable andslower for NREM stage.

Since NREM stage constitutes the majority (about 75% to 80%) of totalsleep for typical healthy adults, the average breathing rate during NREMstage can be estimated by localizing the peak of the histogram ofovernight breathing rate estimates, as shown in FIG. 24A. On this basis,one can define breathing rate deviation, the distance between theestimated average NREM breathing rate and the 90% tile of the breathingrate for each epoch, to quantify the deviation of the breathing rateduring REM stage from that during NREM stage.

To extract the variability of the breathing rate for each epoch, one canfirst estimate the trend of breathing rate by applying a low pass filterto the breathing estimates of the whole night, and obtain the detrendedbreathing rate estimates by subtracting the trend from the originalbreathing rate estimates. Then, the breathing rate variability isdefined and calculated for each epoch as the variance of the detrendedestimates normalized by the length of epoch.

FIG. 24B visualizes the distribution of the proposed two features underNREM and REM sleep, respectively. As one can see from FIG. 24B, themajority of the breathing rate variability and breathing rate deviationof NREM sleep are much smaller than those of REM sleep. Based on thesetwo features, one can train a support vector machine (SVM), a widelyused binary classifier, to differentiate between REM and NREM sleep.

Sleep Quality Assessment

When one obtains the estimates of wake, REM, and NREM stages of a wholesleep, one can assess the elusive sleep quality for a user by followingstandard approach used in clinical practice. In particular, one cancalculate the sleep score for each night based on the recognized sleepstages as follows. Let T_(N), T_(R) and T_(W) denote the durations(measured in hours) of NREM sleep, REM sleep and wake, respectively.Since there is no standard formula for sleep score calculation, a simpleformula for the sleep score is applied in SMARS:S=10*T _(N)+20*T _(R)−10*T _(W),  (14)which means that the more you sleep, the more you have REM sleep, theless you keep awake in the bed, the better your sleep score is.According to recent research, REM sleep is crucial for mental recovery,and thus a higher weight has been assigned to REM sleep.

SMARS envisions a practical sleep monitoring for daily in-home use.Although it does not make much sense to compare the sleep score amongdifferent users, the trend or history of the sleep score for aparticular user would reflect the changes of his/her sleep quality. Suchresults provide clinically meaningful evidences to help diagnose sleepdisorders and manage personal health, in an attractive way. FIG. 25illustrates an exemplary network environment for sleep monitoring,according to one embodiment of the present teaching. FIG. 26 illustratesan exemplary algorithm design for sleep monitoring, according to oneembodiment of the present teaching.

FIG. 27 illustrates an exemplary interior bird-view of a car for seatoccupancy detection and people counting, according to one embodiment ofthe present teaching. In an example, one or more Type 1 devices andmultiple Type 2 devices (e.g. N=4) may be placed in a confined area(e.g. closed area such as a car, a conference room, a bus, an airplane,or a cinema, semi-open area such as a bus with windows open, or abalcony with 8 outdoor chairs around an outdoor table) with multiple“seats” at fixed locations (may be a space for standing, sitting,kneeling, lying down, etc.,) that may hold a person. A presence of aperson at one or more “seats” may be detected based on time series of CIextracted from wireless signals sent from the Type 1 devices to the Type2 devices.

FIG. 27 shows a particular example of a car with 4 seats, two in frontrow and two in back row. Note that each seat has a “seat bed” for aperson to sit on and a “seat back” for a person to lean back on. TheType 1 device may be placed in the front, on the dash board. Four Type 2devices may be deployed, one at each of the 4 seats (e.g. in/on/underthe seat bed, or in/on/under the seat bed). When a seat A (e.g. driverseat, or right seat on front row, or back row left seat, etc.) isoccupied by the driver or a passenger or a baby in a car-seat, the CI(channel information) associated with the occupied seat A may behavedifferently (e.g. become smaller or bigger) from the CI associated withan empty seat A. Thus one can detect the seat occupancy of each seat byexamining the CI. By performing such test for all the seats, one cancount the amount of people in the car. If a person sits at non-standardlocations (e.g. between two seats, in the center of back row, in thecenter of front row, or a baby in a car seat), CI associated multipleType 2 devices can be analyzed jointly to determine if there is a personthere. As signature of a baby in car-seat may be different from an adultor a children, adult/children/baby classification may be performed basedon the CI.

A task may be performed based on the seat occupancy described above. Forexample, the task may be to arm an air-bag if a seat is occupied, butdisarm the airbag if the seat is not occupied. If a small size person(e.g. a child) instead of a regular-size adult is detected, an air-bagdesigned for adults may not be armed. The heating/air-condition settingmay be adjusted. The task may be to control windows, lighting, audiosystem, entertainment system (e.g. video), noise cancelling,shock-absorbing system, stabilizing size, car avoidance system, safetyfeatures, tire pressure, any other car subsystems, etc. For example, ifa passenger is detected at the front right seat, the temperature at thatregion (front, right) may be controlled to a preset level. If the seatis empty, the temperature may be adjusted differently.

FIGS. 28A-28D illustrate changes of channel information (CI) accordingto various seat occupancy situations in a car, according to oneembodiment of the present teaching. FIG. 28A shows CI when no seat isoccupied. FIG. 28B shows CI when seat 1 (e.g. seat with Bot antenna 1 inFIG. 27) is occupied. FIG. 28C shows CI when seat 3 (e.g. seat with Botantenna 3 in FIG. 27) is occupied. FIG. 28D shows CI when both seat 1and seat 3 are occupied.

The features described above may be implemented advantageously in one ormore computer programs that are executable on a programmable systemincluding at least one programmable processor coupled to receive dataand instructions from, and to transmit data and instructions to, a datastorage system, at least one input device, and at least one outputdevice. A computer program is a set of instructions that may be used,directly or indirectly, in a computer to perform a certain activity orbring about a certain result. A computer program may be written in anyform of programming language (e.g., C, Java), including compiled orinterpreted languages, and it may be deployed in any form, including asa stand-alone program or as a module, component, subroutine, abrowser-based web application, or other unit suitable for use in acomputing environment.

Suitable processors for the execution of a program of instructionsinclude, e.g., both general and special purpose microprocessors, digitalsignal processors, and the sole processor or one of multiple processorsor cores, of any kind of computer. Generally, a processor will receiveinstructions and data from a read-only memory or a random access memoryor both. The essential elements of a computer are a processor forexecuting instructions and one or more memories for storing instructionsand data. Generally, a computer will also include, or be operativelycoupled to communicate with, one or more mass storage devices forstoring data files; such devices include magnetic disks, such asinternal hard disks and removable disks; magneto-optical disks; andoptical disks. Storage devices suitable for tangibly embodying computerprogram instructions and data include all forms of non-volatile memory,including by way of example semiconductor memory devices, such as EPROM,EEPROM, and flash memory devices; magnetic disks such as internal harddisks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROMdisks. The processor and the memory may be supplemented by, orincorporated in, ASICs (application-specific integrated circuits).

While the present teaching contains many specific implementationdetails, these should not be construed as limitations on the scope ofthe present teaching or of what may be claimed, but rather asdescriptions of features specific to particular embodiments of thepresent teaching. Certain features that are described in thisspecification in the context of separate embodiments may also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment mayalso be implemented in multiple embodiments separately or in anysuitable sub-combination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems maygenerally be integrated together in a single software product orpackaged into multiple software products.

Particular embodiments of the subject matter have been described. Anycombination of the features and architectures described above isintended to be within the scope of the following claims. Otherembodiments are also within the scope of the following claims. In somecases, the actions recited in the claims may be performed in a differentorder and still achieve desirable results. In addition, the processesdepicted in the accompanying figures do not necessarily require theparticular order shown, or sequential order, to achieve desirableresults. In certain implementations, multitasking and parallelprocessing may be advantageous.

We claim:
 1. A system for monitoring object motion in a venue,comprising: a transmitter located at a first position in the venue andconfigured for transmitting a first wireless signal through a wirelessmultipath channel, wherein the wireless multipath channel is impacted bya pseudo-periodic motion of an object in the venue; a receiver locatedat a second position in the venue and configured for: receiving a secondwireless signal through the wireless multipath channel, wherein thesecond wireless signal is determined based on both the wirelessmultipath channel impacted by the pseudo-periodic motion of the objectin the venue and the first wireless signal, and extracting at least onetime series of channel information (CI) of the wireless multipathchannel from the second wireless signal, wherein each CI comprises atleast one of: a channel state information (CSI), a frequency domain CSI,a frequency domain CSI associated with at least one sub-band, a timedomain CSI, a channel impulse response (CIR), a channel frequencyresponse (CFR), a channel characteristics, and a channel filterresponse, of the wireless multipath channel; and a vital sign estimatorconfigured for: determining that at least one portion of the at leastone time series of CI (TSCI) in a current sliding time window isassociated with the pseudo-periodic motion of the object in the venue,and computing a current characteristics related to the pseudo-periodicmotion of the object in the current sliding time window based on atleast one of: the at least one portion of the at least one TSCI in thecurrent sliding time window, at least one portion of the at least oneTSCI in a past sliding time window, and a past characteristics relatedto the pseudo-periodic motion of the object in the past sliding timewindow.
 2. The system of claim 1, wherein: the object is at least oneof: a life, a device, and a land; the pseudo-periodic motion representsat least one of the following of the object: breathing, heartbeat,periodic hand gesture, periodic gait, rotation, vibration, andearthquake; and the current characteristics represents a frequency ofthe pseudo-periodic motion.
 3. The system of claim 1, wherein: the vitalsign estimator is coupled to at least one of: the transmitter, thereceiver, an additional transmitter, an additional receiver, a cloudserver, a fog server, a local server, and an edge server; and at leastone of the current characteristics and the past characteristicscomprises information related to at least one of: a frequency ofpseudo-periodic motion, a frequency characteristics, a frequencyspectrum, a time period of pseudo periodic motion, a temporalcharacteristics, a temporal profile, a timing of pseudo-periodic motion;a starting time, an ending time, a duration, a history of motion, amotion type, a motion classification, a location of the object, a speed;a displacement, an acceleration, a rotational speed, a rotationalcharacteristics, a gait cycle of the object, a transient behavior of theobject, a transient motion, a change in pseudo-periodic motion, a changein frequency of pseudo-periodic motion, a change in gait cycle, an eventassociated with pseudo-periodic motion, an event associated withtransient motion, a sudden-motion event, and a all-down event.
 4. Thesystem of claim 1; wherein the vital sign estimator comprises: a channelinformation processor, a spectral analyzer, and an energy spectrumnormalizer that are configured for processing the at least one TSCI inthe current sliding time window on both time domain and frequencydomain; and a peak detector configured for detecting at least one peakin the frequency domain, wherein the current characteristics related tothe pseudo-periodic motion of the object is computed based on the atleast one peak in the frequency domain.
 5. The system of claim 1,wherein the vital sign estimator comprises a channel informationprocessor configured for, for each CI of a particular portion of the atleast one TSCI in the current sliding time window: obtaining N1frequency domain components of the CI; determining a timestampassociated with the CI; and preprocessing the N1 frequency domaincomponents, wherein the preprocessing comprises: cleaning phases of theN1 frequency domain components, and normalizing the N1 frequency domaincomponents such that the N1 frequency domain components have a unitytotal power, wherein the current characteristics related to thepseudo-periodic motion of the object is computed based on thepreprocessed N1 frequency domain components of the CI of the particularportion of the at least one TSCI in the current sliding time window. 6.The system of claim 1, wherein the vital sign estimator comprises aspectral analyzer configured for: for each CI of a particular portion ofthe at least one TSCI in the current sliding time window: converting N1frequency domain components of the channel information using an inversefrequency transform to N2 time domain coefficients of the channelinformation, wherein: each channel information is associated with atimestamp, N2 is not smaller than N1, each of the N2 coefficients isassociated with a time delay, and the inverse frequency transformcomprises at least one of: inverse Fourier transform, inverse Laplacetransform, inverse Hadamard transform, inverse Hilbert transform,inverse sine transform, inverse cosine transform, inverse triangulartransform, inverse wavelet transform, inverse integer transform, inversepower-of-2 transform, combined zero padding and transform, and inverseFourier transform with zero padding, and retaining first C of the N2time domain coefficients, wherein C is not larger than N2; andcorrecting timestamps of all channel information of the particularportion of the at least one TSCI in the current sliding time window sothat the corrected timestamps of time-corrected channel information areuniformly spaced in time, wherein correcting the timestamps comprises:identifying a particular CI with a particular timestamp to be replacedby a time-corrected CI with a corrected timestamp, and computing thetime-corrected CI by computing the C retained time domain coefficientsof the time-corrected CI at the corrected timestamp using aninterpolation filter associated with the corrected timestamp, whereinthe current characteristics related to the pseudo-periodic motion of theobject is computed based on the C retained time domain coefficients ofthe CI of the particular portion of the at least one TSCI in the currentsliding time window.
 7. The system of claim 6, wherein the spectralanalyzer is further configured for: for each of the C retained timedomain coefficients: identifying a corresponding retained time domaincoefficient of each CI of the particular portion of the at least oneTSCI in the current sliding time window, applying bandpass filtering toall the corresponding identified retained time domain coefficients, andapplying a frequency transform to all the corresponding identifiedretained time domain coefficients, wherein a length of the frequencytransform is not smaller than a length of the portion, wherein thecurrent characteristics related to the pseudo-periodic motion of theobject is computed based on outputs of the frequency transform appliedto each corresponding retained time domain coefficient of the CI of theparticular portion of the at least one TSCI in the current sliding timewindow.
 8. The system of claim 7, wherein the vital sign estimatorfurther comprises an energy spectrum normalizer, and wherein thespectral analyzer and the energy spectrum normalizer are configured for:processing the outputs of the frequency transform applied to eachcorresponding retained time domain coefficient of the CI of theparticular portion of the at least one TSCI in the current sliding timewindow, wherein the frequency transform comprises at least one of:Fourier transform, Laplace transform, Hadamard transform, Hilberttransform, sine transform, cosine transform, triangular transform,wavelet transform, integer transform, power-of-2 transform, combinedzero padding and transform, and Fourier transform with zero padding,wherein the processing comprises at least one of: preprocessing,processing, post-processing, filtering, linear filtering, nonlinearfiltering, folding, grouping, energy computation, low-pass filtering,bandpass filtering, high-pass filtering, matched filtering, enhancement,restoration, de-noising, spectral analysis, inverse linear transform,nonlinear transform, feature extraction, machine learning, recognition,labeling, training, clustering, grouping, sorting, thresholding,comparison with time-corrected channel information of another portion ofanother time series of channel information in another sliding timewindow, similarity score computation, vector quantization, compression,encryption, coding, storing, transmitting, representing, merging,combining, splitting; restricting to selected frequency band, spectrumfolding, averaging, averaging over selected frequency, averaging overselected time domain coefficients, and averaging over antenna links. 9.The system of claim 8, wherein: the transmitter has at least oneantenna; the receiver has at least one antenna; each of the at least oneTSCI is associated with one of the at least one antenna of thetransmitter and one of the at least one antenna of the receiver; theaveraging over antenna links is weighted averaging over the at least oneTSCI; and the averaging over selected time domain coefficients isweighted averaging over the C retained time domain coefficients.
 10. Thesystem of claim 9, wherein the vital sign estimator further comprises apeak detector configured for: identifying at least one local maximum andat least one local minimum the frequency domain; computing at least onelocal signal-to-noise-ratio-like (SNR-like) parameter for each pair of alocal maximum and a local minimum adjacent to each other; identifyingsignificant local peaks each being at least one of: a local maximum withSNR-like parameter greater than a first threshold T1 and a local maximumwith an amplitude greater than a threshold T2.
 11. The system of claim10, wherein: the at least one local maximum and the at least one localminimum are identified in the frequency domain using a peak detectionapproach.
 12. The system of claim 10, wherein the vital sign estimatorfurther comprises a breathing rate estimator configured for: selecting aset of selected significant local peaks from the set of identifiedsignificant local peaks based on a selection criterion, wherein thecurrent characteristics related to the pseudo-periodic motion of theobject is computed based on the set of selected significant local peaksand frequency values associated with the set of selected significantlocal peaks.
 13. The system of claim 12, wherein the breathing rateestimator is further configured for: computing information associatedwith a remaining spectrum with the set of selected significant localpeaks removed; and detecting an event associated with the currentcharacteristics related to the pseudo-periodic motion of the objectbased on at least one of: a remaining spectral energy of the remainingspectrum, an adaptive threshold, and a finite state machine, where theevent comprises at least one of: a presence, an absence, an appearance,a disappearance, a steady behavior and a non-steady behavior of at leastone of: non-periodic motion, transient motion, strong motion, weakmotion; strong background interference and another periodic motion. 14.The system of claim 13, further comprising a system state controllerconfigured for: computing adaptively at least one of: decisionthresholds, threshold T1, threshold T2, lower bound associated withfrequency of the selected significant local peaks, upper boundassociated with frequency of the selected significant local peaks,search range, and a parameter associated with a selection of theselected significant local peaks, based on a finite state machine (FSM),wherein the FSM comprises at least one of: an Initiation (DIT) state, aVerification state, a PeakFound state, and a Motion state.
 15. Thesystem of claim 14, wherein the system state controller is furtherconfigured for: entering the INIT state of the FSM in at least onepredetermined way; computing the thresholds adaptively in a first way inthe INIT state; detecting an event based on the thresholds;transitioning from the INIT state to a different state of the FSM basedon at least one transition criterion; and computing the thresholdsadaptively in a second way in the different state.
 16. The system ofclaim 14, wherein the system state controller is further configured for:entering the INIT state of the FSM; computing the thresholds adaptivelyin a first way; detecting an event based on the thresholds and at leastone of: the set of selected significant local peaks and relatedcharacteristics; detecting excessive background interfering motion basedon the thresholds and the remaining spectral energy; staying in the INITstate when the event is concluded to be “not detected” and the excessivebackground interfering motion is concluded to be “not detected;”transitioning from the INIT state to the Verification state when theevent is concluded to be “detected” preliminarily and the detected eventneeds to be verified; and transitioning from the INIT state to theMotion state when the excessive background interfering motion isconcluded to be “detected.”
 17. The system of claim 16, wherein thesystem state controller is further configured for: in the Verificationstate: computing the thresholds adaptively in a second way; accumulatingand computing at least one statistics based on sets of selectedsignificant local peaks and related characteristics in at least oneadjacent sliding time window for verification of the detected event thatis detected preliminarily; staying in the Verification state while theat least one statistics is being accumulated until sufficient statisticsis collected for the verification; verifying the preliminarily detectedevent based on the thresholds and the at least one statistics;transitioning from the Verification state to the PeakFound state whenthe preliminarily detected event is concluded as “verified;”transitioning from the Verification state to the INIT state whenverification is concluded to be “not verified;” and staying in theVerification state when verification is not concluded.
 18. The system ofclaim 17, wherein the system state controller is further configured for:in the PeakFound state: computing the thresholds adaptively in a thirdway; detecting the verified event based on the thresholds and the atleast one of: the set of selected significant local peaks and relatedcharacteristics; detecting the excessive background interfering motionbased on the thresholds and the remaining spectral energy; staying inthe PeakFound state when the verified event is concluded as “detected;”transitioning from the PeakFound state to the INIT state when theverified event is concluded as “not detected” for a number of timeinstances; and transitioning from the PeakFound state to the Motionstate when the excessive background interfering motion is concluded as“detected” for a number of time instances.
 19. The system of claim 16,wherein the system state controller is further configured for: in theMotion state: computing the thresholds adaptively in a fourth way;detecting the excessive background interfering motion based on thethresholds and the remaining spectral energy; staying in the Motionstate when the excessive background interfering motion is concluded as“detected;” and transitioning from the Motion state to the INIT statewhen the excessive background interfering motion is concluded as “notdetected” for a number of time instances.
 20. The system of claim 1,wherein the vital sign estimator further comprises a spectral analyzerconfigured for: determining a timestamp associated with each channelinformation of the at least one TSCI; correcting timestamps of allchannel information of a particular portion of the at least one TSCI inthe current sliding time window so that the corrected timestamps oftime-corrected channel information are uniformly spaced in time; andperforming an operation on the time-corrected channel information withrespect to the corrected timestamps, wherein the current characteristicsrelated to the pseudo-periodic motion of the object is computed based onan output of the operation, wherein the operation comprises at least oneof: preprocessing, processing, post-processing, filtering, linearfiltering, nonlinear filtering, low-pass filtering, bandpass filtering,high-pass filtering, matched filtering, enhancement; restoration,de-noising, spectral analysis, frequency transform, inverse frequencytransform, linear transform, nonlinear transform, feature extraction,machine learning, recognition, labeling, training, clustering, grouping,sorting, thresholding, peak detection, comparison with time-correctedchannel information of another portion of another time series of channelinformation in another sliding time window, similarity scorecomputation, vector quantization, compression, encryption, coding,storing, transmitting, representing, merging, combining, fusion, linearcombination, nonlinear combination, and splitting.
 21. The system ofclaim 1, wherein the vital sign estimator is further configured for:making the current characteristics related to the pseudo-periodic motionof the object available in real time; and moving the current slidingtime window by a shift-size as time progresses.
 22. The system of claim1, further comprising: an additional transmitter located at a thirdposition in the venue and configured for transmitting a third wirelesssignal through the wireless multipath channel impacted by thepseudo-periodic motion of the object in the venue; an additionalreceiver located at a fourth position in the venue and configured for:receiving a fourth wireless signal through the wireless multipathchannel, wherein the fourth wireless signal is determined based on boththe wireless multipath channel and the third wireless signal, andobtaining an additional TSCI of the wireless multipath channel based onthe fourth wireless signal, wherein the vital sign estimator is furtherconfigured for: determining that at least one portion of the additionalTSCI in the current sliding time window is associated with thepseudo-periodic motion of the object in the venue, and computing thecurrent characteristics related to the pseudo-periodic motion of theobject in the current sliding time window based on at least one of: theat least one portion of the additional TSCI in the current sliding timewindow, at least one portion of the additional TSCI in an additionalpast sliding time window, and a past characteristics related to thepseudo-periodic motion of the object in the additional past sliding timewindow.
 23. The system of claim 1, wherein the vital sign estimator isfurther configured for: determining that at least one portion of the atleast one TSCI in the current sliding time window is associated with anadditional pseudo-periodic motion of an additional object in the venue,wherein the wireless multipath channel is further impacted in thecurrent sliding time window by the additional pseudo-periodic motion ofthe additional object; and computing a current characteristics relatedto the additional pseudo-periodic motion of the additional object in thecurrent sliding time window based on at least one of: the at least oneportion of the at least one TSCI in the current sliding time window, theat least one portion of the at least one TSCI in the past sliding timewindow, and a past characteristics related to the additionalpseudo-periodic motion of the additional object in the past sliding timewindow.
 24. The system of claim 23, wherein the vital sign estimator isfurther configured for: determining that the wireless multipath channelis impacted in the current sliding time window by pseudo-periodicmotions of a plurality of objects; computing current characteristicsrelated to pseudo-periodic motions of the plurality of objects in thecurrent sliding time window based on at least one of: the at least oneportion of the at least one TSCI in the current sliding time window, theat least one portion of the at least one TSCI in the past sliding timewindow, and a past characteristics related to the pseudo-periodicmotions of the plurality of objects in the past sliding time window; andestimating a quantity of the plurality of objects based on the currentcharacteristics.
 25. A method for monitoring object motion in a venue,comprising: receiving a wireless signal as an output of a wirelessmultipath channel, wherein the wireless multipath channel is impacted bya pseudo-periodic motion of an object in the venue; extracting at leastone time series of channel information (CI) of the wireless multipathchannel from the wireless signal, wherein each CI comprises at least oneof: a channel state information (CSI), a frequency domain CSI, afrequency domain CSI associated with at least one sub-band, a timedomain CSI, a channel impulse response (CIR), a channel frequencyresponse (CFR), a channel characteristics, and a channel filterresponse, of the wireless multipath channel; determining that at leastone portion of the at least one time series of CI (TSCI) in a currentsliding time window is associated with the pseudo-periodic motion of theobject in the venue; and computing a current characteristics related tothe pseudo-periodic motion of the object in the current sliding timewindow based on at least one of: the at least one portion of the atleast one TSCI in the current sliding time window, at least one portionof the at least one TSCI in a past sliding time window, and a pastcharacteristics related to the pseudo-periodic motion of the object inthe past sliding time window.
 26. The method of claim 25, furthercomprising: processing the at least one TSCI in the current sliding timewindow on both time domain and frequency domain; and detecting at leastone peak in the frequency domain, wherein the current characteristicsrelated to the pseudo-periodic motion of the object is computed based onthe at least one peak in the frequency domain, wherein at least one ofthe current characteristics and the past characteristics comprisesinformation related to at least one of: a frequency of pseudo-periodicmotion, a frequency characteristics, a frequency spectrum, a time periodof pseudo periodic motion, a temporal characteristics, a temporalprofile, a timing of pseudo-periodic motion, a starting time, an endingtime, a duration, a history of motion, a motion type, a motionclassification, a location of the object, a speed, a displacement, anacceleration, a rotational speed, a rotational characteristics, a gaitcycle of the object, a transient behavior of the object, a transientmotion, a change in pseudo-periodic motion, a change in frequency ofpseudo-periodic motion, a change in gait cycle, an event associated withpseudo-periodic motion, an event associated with transient motion, asudden-motion event, and a fall-down event.
 27. The method of claim 25,further comprising: determining a timestamp associated with each channelinformation of the at least one TSCI; correcting timestamps of allchannel information of a particular portion of the at least one TSCI inthe current sliding time window so that the corrected timestamps oftime-corrected channel information are uniformly spaced in time; andperforming an operation on the time-corrected channel information withrespect to the corrected timestamps, wherein the current characteristicsrelated to the pseudo-periodic motion of the object is computed based onan output of the operation, wherein the operation comprises at least oneof: preprocessing, processing, post-processing, filtering, linearfiltering, nonlinear filtering, low-pass filtering, bandpass filtering,high-pass filtering, matched filtering, enhancement, restoration,de-noising, spectral analysis, frequency transform, inverse frequencytransform, linear transform, nonlinear transform, feature extraction,machine learning, recognition, labeling, training, clustering, grouping,sorting, thresholding, peak detection, comparison with time-correctedchannel information of another portion of another time series of channelinformation in another sliding time window, similarity scorecomputation, vector quantization, compression, encryption, coding,storing, transmitting, representing, merging, combining, fusion, linearcombination, nonlinear combination, and splitting.
 28. A receiver of amotion monitoring system, comprising: a wireless circuitry configured toreceive a wireless signal as an output of a wireless multipath channel,wherein the wireless multipath channel is impacted by a pseudo-periodicmotion of an object in a venue, wherein an input signal of the wirelessmultipath channel is transmitted asynchronously by a transmitter of themotion monitoring system, wherein the transmitter is different from thereceiver and different from the object; a processor communicativelycoupled with the wireless circuitry; a memory communicatively coupledwith the processor; and a set of instructions stored in the memorywhich, when executed, causes the processor to extract at least one timeseries of channel information (CO of the wireless multi path channelfrom the wireless signal, wherein: at least one portion of the at leastone time series of CI (TSCI) in a current sliding time window isassociated with the pseudo-periodic motion of the object in the venue,and the at least one portion of the at least one TSCI in the currentsliding time window is to be used by a vital sign estimator of themotion monitoring system to compute a current characteristics related tothe pseudo-periodic motion of the object in the current sliding timewindow based on at least one of: the at least one portion of the atleast one TSCI in the current sliding time window, at least one portionof the at least one TSCI in a past sliding time window, and a pastcharacteristics related to the pseudo-periodic motion of the object inthe past sliding time window.
 29. The receiver of claim 28, wherein thevital sign estimator is coupled to the receiver and configured for:processing the at least one TSCI in the current sliding time window onboth time domain and frequency domain; and detecting at least one peakin the frequency domain, wherein the current characteristics related tothe pseudo-periodic motion of the object is computed based on the atleast one peak in the frequency domain, wherein at least one of thecurrent characteristics and the past characteristics comprisesinformation related to at least one of: a frequency of pseudo-periodicmotion, a frequency characteristics, a frequency spectrum, a time periodof pseudo periodic motion, a temporal characteristics, a temporalprofile, a timing of pseudo-periodic motion, a starting time, an endingtime, a duration, a history of motion, a motion type, a motionclassification, a location of the object, a speed, a displacement, anacceleration, a rotational speed, a rotational characteristics, a gaitcycle of the object, a transient behavior of the object, a transientmotion, a change in pseudo-periodic motion, a change in frequency ofpseudo-periodic motion, a change in gait cycle, an event associated withpseudo-periodic motion, an event associated with transient motion, asudden-motion event, and a fall-down event.
 30. An estimator of a motionmonitoring system, comprising: a processor; a memory communicativelycoupled with the processor; and a set of instructions stored in thememory which, when executed, causes the processor to perform: obtainingat least one time series of channel information (TSCI) of a wirelessmultipath channel extracted by a receiver of the motion monitoringsystem from a wireless signal received as an output of the wirelessmultipath channel, wherein the wireless multipath channel is impacted bya pseudo-periodic motion of an object in a venue, and wherein an inputsignal of the wireless multipath channel is transmitted by a transmitterof the motion monitoring system, wherein the transmitter is differentfrom the receiver and different from the object, determining that atleast one portion of the at least one TSCI in a current sliding timewindow is associated with the pseudo-periodic motion of the object inthe venue, and computing a current characteristics related to thepseudo-periodic motion of the object in the current sliding time windowbased on at least one of: the at least one portion of the at least oneTSCI in the current sliding time window, at least one portion of the atleast one TSCI in a past sliding time window, and a past characteristicsrelated to the pseudo-periodic motion of the object in the past slidingtime window.